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Patsit Permpoonsavat

Sood Yacht Patsit Permpoonsavat (สุดยอด พัฐสิฏ เพิ่มพูลสวัสดิ์) is a Thai actor and model who made his acting career debut in 2017 with the series "Please... Siang Riak Winyan", since then he is active.

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Sood Yacht Patsit Permpoonsavat

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He graduated with a bachelor's in computer science from King Mongkut's University of Technology Thonburi. [1]

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Between Us the series – review, plot, cast and where to watch BL drama

One of the fan-favorite second-lead couples in BL land, Boun and Prem, recently answered fans’ prayers as they starred in a brand new show as the main leads in Between Us. Here’s all about the drama, from a mini-review to the stunning cast, plot, and more.

Thailand’s swoon-worthy catalog of BL has never been better. From K-Pop and K-Dramas to C-Dramas and now the Thai BL series, there’s a rising interest in Asian culture entertainment thanks to their vast storylines and rich cinematography.

Here, we are taking a look at BL’s latest offering, Between Us, a parallel of the 2019 series Until We Meet Again.

Warning: Mild spoilers ahead!

• MORE: Fans praise Mile and Apo’s chemistry as KinnPorsche stars grace magazine cover

Between Us the series – Plot

The plot of Between Us the series revolves around a college freshman, Team, who is starting a new chapter of his life as he steps into the university. An exceptional swimmer on scholarship, Team looks like a regular student at first but he comes with a tragic backstory.

The young character carries the heavy weight of years of trauma after losing his cousin during childhood. On what was meant to be a casual swim and day out, the incident turned into a huge nightmare for Team which haunts him along the course of the series.

However, his dark sleepless nights sees the light when he comes across an upperclassman. Win is the vice president of the swimming club, who unknowingly falls for Team at first sight.

The two start off their relationship as “friends with benefits,” looking to only have fun. But the casual fling soon turns into deep love as they find comfort in each other’s company.

• READ NEXT: Katy Louise Saunders and Song Joong Ki expecting first baby as stars get married

Meet the cast

Between Us’ main leads is one of the fan-favorite couples in BL. The talented actors have been winning hearts with their incredible chemistry.

Boun Noppanut Guntachai, 27, stars as the charming and mysterious yet caring, Win.

View this post on Instagram A post shared by noppanut guntachai (@bb0un)

The goofy (always hungry and snacking) and heart-melting character, Team has been played by Prem Warut Chawalitrujiwong, 24.

View this post on Instagram A post shared by PP (@prem_space)

The rest of the cast includes:

• Ohm Thitiwat Ritprasert as Dean
• Fluke Natouch Siripongthon as Pharm
• Bosston Suphadach Wilairat as Pruek
• Samantha Melanie Coates as Manow
• Sood Yacht Patsit Permpoonsavat as Tul
• O Puwanai Sangwan as Wan
• Tae Weerapat Toemmaneerat as Bee
• Benz Panupun Vongjorn as Prince
• Art Pakpoom Juanchainat as A
• Ryu Pakphum Jitpisutsiri as Sea
• Santa Pongsapak Oudompoch as Wiew

Review – Boun and Prem did a fantastic job at bringing WinTeam to life

Now, let’s talk about where Between Us series stands overall in terms of the storyline and performance.

The show makers have done a fantastic job with presenting the timeline of Until We Meet Again and ties both the series well together. Fans may feel nostalgic at some point and wish to also go back to UWMA (we don’t blame them) for Dean and Pharm arc.

Between Us is a swoon-worthy watch that serves a heartwarming story and to-die-for chemistry between the main leads. However, the course of the story falls loose at a few points and the episodes may seem repetitive. But overall, it’s definitely a must-watch thanks to WinTeam’s slow-growing relationship, coupled with a steamy romance.

Notably, the drama has done a commendable job at walking through Team’s childhood trauma and how he struggles with it even after so many years. From Team panicking while swimming to slowly learning to cope, the development will have you in tears. Prem deserves all the praise for his incredible representation of the character, which tugs at the heartstrings.

Boun, on the other hand, will have you thinking “I wish I had a partner like that.” He too has done a fabulous job at representing the character of Win, who has the biggest heart for the people he loves and will go to any length to take care of them. While doing so, he often puts his own needs in the backseat. Boun expressing these emotions through just his eyes, is simply incredible.

Both the stars have done a fantastic job at bringing Win and Team to life. They successfully delivered the raw emotions behind their respective characters. Right from the start, Boun and Prem kept the audiences hooked as fans rooted for them.

• READ MORE: The Spanish television series Elite’s Indian remake is all set to release on Netflix

Where to watch Between Us

Fans can watch Between Us on Studio Wabi Sabi’s official YouTube channel . They have also included a membership option for viewers wanting to watch the uncut version of the show.

As the series recently concluded and if you can’t get enough of Boun and Prem, fans can also watch them in another series titled Even Sun (2022).

By Shriya Swami – [email protected]

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See pics for more Yaya

• Introduction

Yaya (야야), better known as Sood Yacht (สุดยอด), and formerly known as Surat (สุดยอด), is a Thai actor currently managed by GMMTV. Yaya made his acting debut in Thailand in 2017 and progressively rose to stardom through multiple guest and support appearances on various dramas, notably BLs. He gained attention for his portrayal of "Pond" in the 2018 BL drama "Love By Chance". In 2023, Yaya was revealed to be one of the participants of the MBC survival show " Fantasy Boys ". He eventually dropped out from the show due to health issues. On April 15, 2024, his contract was terminated with Studio Wabi Sabi due to the suspension of the artist management department. He was then revealed to have signed a new contract with GMMTV.

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Thai LGBT Series ‘7 Project’ Now Streaming on iQiyi

iQiyi and Studio Wabi Sabi, a Thai production company, recently launched the LGBT series “7 Project.” The romance drama mini-series started streaming on the iQiyi app on August 30.

The drama focuses on campus romance in seven separate episodes and theme songs. Through the connection of music and video, each episode discusses a different love issue with a song as the theme. It presents the diverse appearance of love between men and women, men and men, as well as women and women.

‘7 Project’ Tackles Various Types of Romance

The three-minute trailer of the “7 Project” series features an eye-catching cast lineup of young Thai actors and actresses. They include Boom and Peak from “Make It Right,” Boun and Prem from “Until We Meet Again,” and Perth, Earth, Plan, and Surat from “Love By Chance.”

With love as the main theme, it talks about different types of romance in different stories. It is directed by New Siwaj Sawatmaneekul, a well-known Thai youth drama director. He has produced many popular youth Thai dramas in the past five years. 

Put together by Studio Wabi Sabi, the production company that has produced popular BL Thai dramas that have swept Asia such as “Love By Chance” and “Until We Meet Again,” and iQiyi, a leading streaming platform in Southeast Asia, “7 Project” Thai series is definitely a must-see LGBT romance series. 

Musical-Turned-Mini-Series

Commenting on the collaboration, Parnsuk Tongrob, content director for Thailand, iQiyi, said, “iQiyi is very honored to collaborate with Studio Wabi Sabi on ‘7 Project’. It is a unique and challenging mission. Through ‘7 Project,’ we hope to show our audiences the true nature of unconditional love and relationships. With the outstanding performance of Studio Wabi Sabi’s romantic dramas that have already achieved outstanding results in many markets, iQiyi is confident that this series will not only touch the hearts of our users in Thailand, but also in Southeast Asia.”

Siwaj Sawatmaneekul, the director of Studio Wabi Sabi and “7 Project,” said, “Studio Wabi Sabi is honored to have the opportunity to work with Asia’s leading streaming platform, iQiyi. ‘7 Project’ started out as a musical project. But after digging into the deep meaning behind the lyrics and stories, the seven songs finally took on lives of their own and eventually became a mini-series. Each of the seven songs shows the audience the diversity of love and how it can be given and received in different ways. This project is a first for me, and I’m very excited to be able to show this work through a streaming platform with such a large user base in Southeast Asia.”

These are the seven stories on 7 Project:

‘Would You Be My Love’

Earth Katsamonnat and Santa Pongsapakatsamonnat and Santa Pongsapak

Earth, the troublemaker who loves to skip classes, is assigned by his teacher to take care of Santa, a new transfer student, or he will be expelled from school due to too many absences.

‘Once Upon a Time’

Boun-Noppanut and Prem Warut

Despite not believing in love, Boun, who is traumatized by love due to the divorce of his parents, meets Prem, the true love of his life.

Sammy-Samantha Melanie Coates, Pineare-Pannin Charnmanoon, and Ryu-Pakphum Jitpisutsiri

Sammy finds out that her ex-boyfriend Ryu had a one-night stand with another girl and decides to end the relationship and start her life over. She then meets Pineare at school and the two of them gradually develop a crush on each other, but Ryu comes back, and Sammy realizes that Pineare is the girl Ryu had a one-night stand with.

‘Rhythm’s Heart ’

Sood Yacht-Surat Permpoonsavat, Maya-Chanitcha Pimthong, and Aomsin-Waratchaya Noliam

Sood Yacht, a record company owner, often helps Maya, a young actress who is just starting her career, to solve her love and life problems. As the relationship becomes more and more intense, he finally finds himself completely smitten by Maya and begins to listen to his heart that is falling in love with the actress.

‘Break Up Zone ’

Plan-Rathavit Kijworalak, Nink-Varinthorn Makornsirisri, and Pang-Panadda Kaeward

Playboy Plan is hurt in a sincere relationship. He seeks comfort and advice from his best friend Pang, but Pang also encounters frustration in his relationship at the same time. When the two wounded hearts meet each other, their relationship gradually starts changing.

Boom-Krittapak Udompanich, Peak Peemapol Panichtamrong, and Kris-Kris Songsamphant

Boom and Peak, who are at odds with each other, face a struggle over their relationships and classes at school. They suddenly find out that they’re in love with each other, turning from enemies into supportive lovers.

‘50% My Puppy Lov

Perth Tanapon Sukumpantanasan, Fairy-Kirana Pipityakorn, and Mix-Wanut Sangtianprapai

It is a love story that many people experience when they are in school. It depicts Perth, a marginalized person, who falls in love with Fairy, who is being pursued by many people at school. Perth mistakenly thinks that heartthrob Mix also likes Fairy and is afraid to pursue her. But the truth is Fairy has already secretly fallen in love with Perth.

iQiyi has been actively exploring high-quality original content in Asia and promoting Asian content in various languages to 191 countries and regions around the world. This year, the original Korean drama “My Roommate is a Gumiho” quickly rose to the top ten rankings in various regions, attracting drama fans and creating another drama craze for upcoming Chinese dramas “Danger Zone” and “The Ferryman: Legends of Nanyang.”

In the Southeast Asian market, iQiyi has seen the charm of Thai dramas since last year. iQiyi has not only exclusively broadcasted the social issue-focused drama “Daughters,” the Thai dramas “Romantic Blue The Series” and “GEN Y The Series” that were broadcasted on the international version of iQiyi were well-received by the audience. 

Download the iQiyi app or log in to www.iQ.com   for more amazing Asian shows.

Follow iQiyi Philippines on social media: Facebook: iQIYIphilippines Instagram: @iqiyiphilippines Twitter: @iQIYIph

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There are routinely a lot of dating rumors surrounding trending celebs. On this page you will discover is Sood Yacht dating, who is Sood Yacht’s girlfriend, and we’ll take look at his previous relationships, ex-girlfriends, dating rumors, and more. ⤵

Many celebrities have seemingly caught the love bug recently and it seems love is all around us this year. Dating rumors of Sood Yachts’s girlfriends, hookups, and ex-girlfriends may vary since numerous celebrities prefer to keep their lives and relationships private. We strive to make our dating information accurate. Let’s take a look at Sood Yacht’s current relationship below.

Sood Yacht’s girlfriend in 2024

The 27-year-old actor is possibly single now. Sood Yacht remains relatively private when it comes to his love life. Regardless of his marital status, we are rooting for his.

If you have new or updated information on Sood Yacht dating status, please send us a tip .

🧡 Dating fact: On average, daters will kiss on a second date.

Sood Yacht dating compatibility

Sood Yacht is a Aries ♈, which is ruled by planet Mars ♂. Aries' are most romantically compatible with two fire signs, Leo, and Aquarius. This star sign love the chase, and tends to rush into love quickly, but just as quickly as they fall out of love. Pisces least compatible signs for dating: Cancer and Capricorn.

Sood’s zodiac animal is a Ox 🐂. The Ox is the second of all zodiac animals and is reserved, shy, and sometimes may seem boring. Ox's romantic and friendship potential is most compatible with Rat, Snake, and Rooster signs, according to Chinese zodiac compatibility rules. However, people of Goat, Horse, and Dog signs should be avoided when choosing a partner.

💌 Pick up line #239: You're so sweet, you'd put Hershey's out of business!

Sood Yacht past relationships

Based on information available to us, Sood Yacht had at least few relationships in the past few years. Not all details about Sood’s past relationships, partners, and breakups are known. Availability of these details depend on how private celebrities are, and because of that we may not know some facts. While it’s usually easy to find out who is Sood Yacht dating, it is harder to keep track of all his hookups and exes, however that may not always be the case. Some things may be more public than the others, especially when media is involved.

Sood Yacht has not been previously engaged. He has no children. Information on the past dates and hookups is regularly updated.

💑 Conversation starter #10: Can I buy you a drink?

Sood Yacht bio

Sood Yacht was born in the Spring of 1997 on Friday, March 28 🎈 in Thailand 🗺️. His given name is Sood Yacht, friends call his Sood. Thai television actor and social media star who became known for his role on the series Love by Chance. He’s wildly popular on Instagram where he shares selfies, travel and lifestyle pictures with over 330,000 followers. Sood Yacht is most recognized for being a actor. He began posting vlogs on the Longkaek YouTube channel.

Here are more cool facts about Sood.

He was born in the Millennials Generation. When it comes to dating, there are a lot of interesting facts associated with Millennials generation. This age group (1981 - 1996) is dedicating more time to careers, social lives, and personal time with themselves. This generation have not been the ones to rush into marriage. Majority, over 80%, of Millennials view the single life as something that is beneficial.

These are frequently asked questions about Sood Yacht. We will continue to add more related and interesting questions over time.

What is Sood Yacht’s real name? His full/given name is Sood Yacht.

Is Sood Yacht single or dating? Sood Yacht is single.

How many people has Sood Yacht dated? Sood Yacht had at the minimum few relationships, but we cannot be 100% sure exactly how many.

Does Sood Yacht have any children? He has no children.

Did Sood Yacht have any affairs? This information is not available. Contact us if you know something.

What is Sood Yacht’s Life Path number? Sood Yacht’s personality number is 3.

💘 Fun Fact: Did you know the most common time for breakups is right around Valentine's Day and the two weeks before Christmas.

💡 Tip: refresh the page to view more fun and random pick up lines, facts, and conversation starters above.

Come back often as we will continue to update this page with the latest dating news, rumors, and gossip. Help us keep this page updated, by sending any new dating updates .

© 2024 Copyright

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Water Boyy The Series (Thailand) 2017

Water Boyy The Series is Thailand drama premiere on Apr 9, 2017 on GMM One. Cast members: New Thitipoom Techaapaikhun, White Nawat Phumphothingam, Victor Chatchawit Techarukpong, Earth Pirapat Watthanasetsiri, Fon Sananthachat Thanapatpisal, Piglet Charada Imraporn...

Plot Synopsis by DramaWiki Staff ©

This series is based on the 2015 movie Waterboyy. Waii is the most popular member and captain of the Ocean College swimming club. He has a difficult relationship with his father who is also the team’s coach. Apo is the latest swimmer to join the club and Waii’s new roommate.

Support Role

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Sood Yacht’s Girlfriend: Who is Sood Yacht Dating?

There are usually many dating news and scandals surrounding exciting celebrities. The most frequently asked questions are, is Sood Yacht single or dating, and who is Sood Yacht’s girlfriend? We are here to clear up and debunk dating rumors surrounding Sood’s love life and girlfriends.

Everything you need to know about Sood Yacht and a look at Sood Yacht’s relationships, dating timeline, affairs, exes, biography, and more. ⤵

Sood Yacht biography

Sood was born in the Millennials Generation, his zodiac animal is a Ox, and his spirit animal is a Hawk. People born in the year of the ox are dedicated workers who take their responsibilities seriously. Even if they encounter difficulties at work, they will continue. They aren't very romantic, but they are patient and would never impose themselves on someone they didn't like. They are normally silent and keep their emotions hidden. Others find it difficult to understand them. When they lose their cool, it's like an explosion that scares people away. People born on March 28 have a zodiac sign of Aries . Aries, the first sign in the zodiac, likes being number one. Naturally, this powerful fire sign is not without rivalry. Aries, the bold and ambitious sign, pushes straight into even the most difficult situations—and they always come out on top!

Thai television actor and social media star who became known for his role on the series Love by Chance. He’s wildly popular on Instagram where he shares selfies, travel and lifestyle pictures with over 330,000 followers. He posted an Instagram photo with his mom on Mother’s Day 2018.

There are many celebrity romances that blossom every year. Let us take a look at Sood’s dating past below.

Who is Sood Yacht dating?

According to our records, the 27-year-old actor is most likely single now. Sood Yacht remains relatively quiet when it comes to sharing his personal life and makes it a point to stay out of the public eye. He may not be dating anyone publicly, but Sood may be seeing someone in private, and details were not made public yet. So it’s probably not safe to jump to conclusions.

The details about Sood Yachts’s girlfriends and past relationships vary, so we can never be 100% sure. We use multiple online sources such as DatingRumor.com , and other publicly available data to ensure that our dating info and facts are accurate.

Our goal is to make our dating information precise and updated. This page was updated in June 2024 with the latest dating information and news. If you have new details about who Sood Yacht is dating today, please email us .

Sood Yacht girlfriend & dating history

Not all is known about Sood Yacht’s past relationships and partners. While it’s usually simple to find out who Sood is dating, it is harder to track all his breakups, hookups, and flings. Even in 2024, celebrities manage to astonish us with the way they keep their lives private.

Fact: The most common time for a couple to split is right around the two-year mark.

Insights about Sood Yacht

Is Sood Yacht single or dating? Sood Yacht is single.

How many relationships did Sood Yacht have? Sood Yacht had at least few relationship in the past.

How many children does Sood Yacht have? He has no children.

Did Sood Yacht have any affairs? This information is not available.

What is Sood Yacht’s zodiac sign? His zodiac sign is a Aries with a ruling planet of Mars. According to the Chinese horoscope, Sood’s zodiac animal is a Ox.

What is Sood’s full name? His real name is Sood Yacht.

What is Sood Yacht’s Net Worth? At CelebsInsights, we don’t track net worth data, and we recommend checking out: NetWorthTotals .

What is Sood Yacht’s life path number? Sood Yacht’s life path number is 3.

Pick up line #88: Hey, how did you do that? (What?) Look so good?

Dating insight: Women will base 55% of their initial impressions of a man on his appearance and body language.

Conversation starter #89: Can I buy you a drink or do you just want the money?

Refresh the page to view more fun and random pick up lines, dating insights, and conversation starters above.

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Sonu Sood talks about relationships being commodity in a heartfelt video

Actor and philanthropist Sonu Sood often narrates emotional and heart-touching stories that resonate with his fans. His passion for writing poems and shayaris is highly appreciated, especially his ability to address contemporary issues through his poetic expressions. Recently, he uploaded a video on his social media account where he spoke intensely about how relationships are commodified and have lost their true value.

The National hero emphasized that friendship is priceless and irreplaceable, and if ever it were to be sold, the world would turn into chaos. "Value your friends, value friendships," he said by the end of the video. His message gives the true importance of friendships and the necessity of valuing and cherishing them. This ability to convey significant messages through poetry has earned him immense admiration and respect from his fans, as reflected in the comments on his latest social media posts.

  View this post on Instagram   A post shared by Sonu Sood (@sonu_sood)

In addition to his philanthropic work, Sonu Sood is gearing up for the release of his upcoming film Fateh , where he serves as the lead actor, writer, director, and producer. The film, which revolves around the concept of cyber crime and its growing threat to India, features Naseeruddin Shah in a pivotal role. He is reportedly playing the role of a hacker, while Jacqueline Fernandez is the lead actress of the actioner, which is set to redefine actioners in the Indian film industry.

Also Read:  Sonu Sood expresses concern about Indians who lost their lives in Kuwait fire incident, urges people to contribute towards their families

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The report says Santa Clara County, but I live in a different county. Is the report still accurate?

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We have purchased a license to the DissoMaster™ Software and are licensed to use the program. Therefore we can help you find out what your support payments should be within the privacy of your home.

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Yes, our support calculations are good for every county in California.

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Take the total number of days per year the Non-Custodial parent has the children and divide by 365. For example, non-custodial parent has children every other weekend (picking children up Friday evening and returning them Sunday evening) you would take three days times 26 (every other weekend per year). You would get 78. Then you would divide 78 by 365 to get 21% visitation. For a timeshare chart, see this page .

Once a year you can request income and expense information (including pay stubs and tax return) from the other parent. Fill out form FL-396 and enclose a blank form FL-150 . Have someone unrelated to your case serve the other parent with the FL-396 and FL-150. After the papers have been served, have the person that served the papers for you complete the Proof of Service form (attached to FL-396). Once being served the papers the other parent has 30 days to return the information to you.

You may find this information on the Family Courts Self-Help website here .

Will the DissoMaster ™ program calculate spousal support?

Yes, the DissoMaster™ program will also calculate the amount of spousal support in your case. When the judge makes his or her judgment, they will take into account California Family Law Code Section 4320 when determining the support amount. You may find that section of code here .

You should include any income from salary, self-employment (net income after normal business expenses), commissions, royalties, bonuses, rents, dividends, interest, pensions, trust income, annuities, workers’ compensation, unemployment insurance, disability insurance, social security benefits, and spousal support received from a different relationship. DO NOT include any child support received for children from another relationship.

Because the DissoMaster™ program automatically calculates the net amount based on the income and tax information data entered.

Because it is used to calculate your tax responsibility and therefore impacts the child and spousal support figures.

Which figures do I use , monthly or yearly?

All figures are the monthly amount. So if your yearly gross salary is $24,000, divide $24,000 by 12 and enter $2,000 in the column. If your daycare expenses are $100 per week, multiply $100 by 52 and divide by 12 and enter $433.

Adjustments to Income (ATI) is income that is sheltered from normal income taxes such as voluntary contributions to IRA accounts, 401k retirement accounts medical savings accounts, etc. The amount of this income is included in the total gross income and also listed on this line to get an accurate calculation of the parent’s tax liability.

You should have your tax return available to fill in the amounts for Itemized Deductions, Other Medical Expenses, Property Tax Expenses, Deductible Interest Expense, Charitable Contribution Expense and Miscellaneous Itemized deductions. It is not absolutely necessary, but will give a more accurate support calculation if this information is included.

Do you accept attachments of W2 forms or paystubs?

No, unfortunately we can not accept attachments of any kind, including W2 forms and paystubs. If you feel you need to look into your paystubs or W2 form more thoroughly you may want to consider obtaining a consultation with a tax attorney or accountant.

The hardship deduction is for children of another relationship you have a legal obligation to support (does not include step-children), usually living with you. It is also for extraordinary medical expenses and uninsured catastrophic losses. Hardship deductions are at the discretion of the judge to grant.

Are regular Monthly Visit , School and Travel Expenses put on the form?

No, this is for extraordinary expenses not the normal costs involved in visits and school expenses. Expenses for long-distance travel expenses (such as airline tickets, etc.) could be included on the form.

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The report says Santa Clara County , but I live in a different county. Is the report still accurate?

Yes, if there is no spousal support involved in your case. The county is only used by to calculate the spousal support amount. Child support is uniform throughout the entire state of California. If you have spousal support involved in your case, Santa Clara County may be listed even though that is not your county. The DissoMaster™ program uses various counties to calculate the spousal support amount which may or may not be the actual county you reside in. When we do your calculation we check and make sure the correct county is used to make your spousal support calculation.

The oldest child is always “Child 1.” If there are three children, Child 1 would be the oldest and Child 3 would be the youngest.

The information you provide to us is transmitted over our secure server in order to guard the privacy of your personal information. See also our Privacy Policy .

No problem. Send us an email or wait until you receive your report and use the order revision form link included with your report. We will make the changes you need at no extra charge! We gladly provide you with up to 10 revisions within 30 days of your original order.

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California Child and Spousal Support Calculations Professionally Done

We are a company located in California that provides child and spousal support calculations using the DissoMaster™ program . We specialize ONLY in California child and spousal support calculations. The DissoMaster™ program has been used in California for over two decades and is widely used by California Family Law Professionals throughout the entire state. All our reports are emailed directly to you in PDF format, usually within hours.

Our goal is to work WITH you to determine if your support payments are what they should be now. We don’t just enter your data into a program, we carefully examine your information and even run different scenarios to make sure that your support is best for your circumstances. We go over your report and try to find things that you may not be aware of or forgot that will make your support payment a better fit for your budget. Time and many other things will change the amount of your support payments, and they may be greater or lower than your last Court Order.

Your support payments could be off by several hundred dollars a month, maybe even more. Most of our clients save thousands of dollars each year by using our services. The only way to know if your support payment is correct is to run a current DissoMaster™ report. See what some of our actual clients save each month here on our Client Savings page .

By using our services you are not getting a “guesstimate” of what your support should be. You are getting the same calculation that the judge will most likely run. We feel you are entitled to get the same child support amount the Court would order, so we use the DissoMaster™ program which is the program used most often by California Family Law Professionals.

DissoMaster™ Support Calculation for $49.95

We provide a comprehensive DissoMaster™ report , including:

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Dydo Services provides parents and spouses a quick, easy and confidential place to find out what their support payment should be. We strive to provide all our clients with prompt, accurate and affordable reports using the California State Certified DissoMaster support calculator program. We dedicate ourselves to providing individual attention to each client in the privacy of their own home or office. We do every support calculation as if it were our own.

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Article Contents

Introduction, supplementary data, author contributions, acknowledgements.

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COVID-19 antibody seroprevalence in Santa Clara County, California

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Eran Bendavid, Bianca Mulaney, Neeraj Sood, Soleil Shah, Rebecca Bromley-Dulfano, Cara Lai, Zoe Weissberg, Rodrigo Saavedra-Walker, Jim Tedrow, Andrew Bogan, Thomas Kupiec, Daniel Eichner, Ribhav Gupta, John P A Ioannidis, Jay Bhattacharya, COVID-19 antibody seroprevalence in Santa Clara County, California, International Journal of Epidemiology , Volume 50, Issue 2, April 2021, Pages 410–419, https://doi.org/10.1093/ije/dyab010

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Measuring the seroprevalence of antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is central to understanding infection risk and fatality rates. We studied Coronavirus Disease 2019 (COVID-19)-antibody seroprevalence in a community sample drawn from Santa Clara County.

On 3 and 4 April 2020, we tested 3328 county residents for immunoglobulin G (IgG) and immunoglobulin M (IgM) antibodies to SARS-CoV-2 using a rapid lateral-flow assay (Premier Biotech). Participants were recruited using advertisements that were targeted to reach county residents that matched the county population by gender, race/ethnicity and zip code of residence. We estimate weights to match our sample to the county by zip, age, sex and race/ethnicity. We report the weighted and unweighted prevalence of antibodies to SARS-CoV-2. We adjust for test-performance characteristics by combining data from 18 independent test-kit assessments: 14 for specificity and 4 for sensitivity.

The raw prevalence of antibodies in our sample was 1.5% [exact binomial 95% confidence interval (CI) 1.1–2.0%]. Test-performance specificity in our data was 99.5% (95% CI 99.2–99.7%) and sensitivity was 82.8% (95% CI 76.0–88.4%). The unweighted prevalence adjusted for test-performance characteristics was 1.2% (95% CI 0.7–1.8%). After weighting for population demographics, the prevalence was 2.8% (95% CI 1.3–4.2%), using bootstrap to estimate confidence bounds. These prevalence point estimates imply that 53 000 [95% CI 26 000 to 82 000 using weighted prevalence; 23 000 (95% CI 14 000–35 000) using unweighted prevalence] people were infected in Santa Clara County by late March—many more than the ∼1200 confirmed cases at the time.

The estimated prevalence of SARS-CoV-2 antibodies in Santa Clara County implies that COVID-19 was likely more widespread than indicated by the number of cases in late March, 2020. At the time, low-burden contexts such as Santa Clara County were far from herd-immunity thresholds.

Seroprevalence studies of Coronavirus Disease 2019 (COVID-19) provide estimates of the extent of infection that are more representative of true transmission than indicated by case numbers.

In late March 2020, the seroprevalence of antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Santa Clara County, California was estimated at 2.8% [95% confidence interval (CI) 1.3–4.2%] after weighting for county demographics and adjusting for test performance (1.5% unadjusted, 95% CI 1.1–2.0%).

These prevalence point estimates imply that 53 000 (95% CI 26 000 to 82 000 using weighted prevalence) people were infected in Santa Clara County by late March—many more than the ∼1200 confirmed cases at the time.

Using the estimated number of infections and the deaths in Santa Clara County at the time, we estimate a local infection fatality rate of 0.17%.

The first two cases of Coronavirus Disease 2019 (COVID-19) in Santa Clara County, California were identified in returning travellers on 31 January and 1 February 2020, and the first COVID-19 death in the county was announced on 9 March. 1 In the following month, nearly 1200 additional cases were identified in Santa Clara County, showing a pattern of rapid case increase that was reflective of community transmission. However, the case definition in Santa Clara and many other locations relies on polymerase chain reaction (PCR)-based tests that check for active infections. 2 In addition, PCR-based tests were initially restricted to those with symptomatic disease. Thus, the true extent of infection remains unknown, as confirmed cases miss those with mild or no symptoms and those who have already recovered from infection.

Measuring the true extent of infection is key for epidemic projections and planning response to the epidemic. For example, early projections suggested that, in the absence of strict measures to reduce transmission, the COVID-19 pandemic could overwhelm existing hospital-bed and intensive-care-unit capacity throughout the USA and lead to >2 million deaths. 3 In the absence of seroprevalence surveys, estimates of the fatality rate in these and other epidemic projections have relied on the number of confirmed cases multiplied by an estimated factor representing unknown or asymptomatic cases to arrive at the number of infections. 4–7 However, the magnitude of that factor is highly uncertain and has been difficult to assess because of three independent processes that introduce measurement error: (i) cases have been diagnosed with PCR-based tests, which do not provide information about resolved infections; (ii) the majority of cases tested early in the course of the epidemic have been acutely ill and highly symptomatic, whereas most asymptomatic or mildly symptomatic individuals have not been tested; and (iii) PCR-based testing rates have been highly variable across contexts and over time, leading to inaccurate relationships between the numbers of cases and infections.

On 3 and 4 April 2020, we conducted a survey of residents of Santa Clara County to measure the seroprevalence of antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and better approximate the number of infections. To the best of our knowledge, this was the first SARS-CoV-2 seroprevalence survey conducted in the USA. At the time of this study, Santa Clara County had the largest number of confirmed cases of any county in Northern California. The county also had several of the earliest known cases of COVID-19 in the state—including one of the first presumed cases of community-acquired disease—making it an especially appropriate location for testing a population-level sample for the presence of active and past infections.

We conducted serologic testing for SARS-CoV-2 antibodies in 3328 adults and children in Santa Clara County using capillary blood draws and a lateral-flow immunoassay. In this section, we describe our sampling and recruitment approaches, specimen-collection methods, antibody-testing procedure, test-kit validation and statistical methods. Our protocol was informed by a World Health Organization protocol for population-level COVID-19 antibody testing. 8 We conducted our study with the cooperation of the Santa Clara County Department of Public Health.

Study participants and sample recruitment

We recruited participants by placing targeted advertisements on Facebook aimed at residents of Santa Clara County. We used Facebook to quickly reach a large number of county residents and because it allows granular targeting by zip code and socio-demographic characteristics. 9 We posted our advertisements targeting two populations: ads aimed at a representative population of the county by zip code and specially targeted ads to balance our sample for under-represented zip codes. In addition, we capped registrations from overrepresented areas after our registration slots filled up quickly with participants from wealthier zip codes. Individuals who clicked on the advertisement were directed to a survey hosted by the Stanford REDCap platform, which provided information about the study. 10 The survey asked for six data elements: zip code of residence, age, sex, race/ethnicity, underlying co-morbidities and prior clinical symptoms. Over 24 hours, we registered 3285 adults, and each adult was allowed to bring one child from the same household with them (889 children registered). Additional details of the participant-selection process are provided below (and in Supplementary Data , available as Supplementary data at IJE online).

Specimen-collection and testing methods

We established drive-through test sites in three locations spaced across Santa Clara County: two county parks in Los Gatos and San Jose, and a church in Mountain View. Only individuals with a participant identification (participant ID) were allowed into the testing area. Verbal informed consent was obtained to minimize participant and staff exposure. With participants in their vehicles, sample collectors in personal protective equipment drew 50–200µL of capillary blood into an EDTA-coated microtainer. Tubes were barcoded and linked with the participant ID. Samples were couriered from the collection sites to a test-reading facility with steady lighting and climate conditions. Technicians drew whole blood up to a fill line on the manufacturer’s pipette and placed it in the test-kit well, followed by a buffer. Test kits were read 12–20 minutes after the buffer was placed. Technicians barcoded tests to match sample barcodes and documented all test results.

Test kit performance

The manufacturer’s performance characteristics were available prior to the study (using 85 confirmed positive and 371 confirmed negative samples). We conducted additional testing to assess the kit performance and continued collecting information from assessments of test performance to incorporate into the analysis. Broadly, test performance was assessed against gold-standard positive specimens from patients with PCR-confirmed COVID-19 (with or without additional confirmation of antibody presence) for sensitivity and gold-standard negative specimens from pre-COVID-era and early-COVID-era specimens. More details on the data provenance, procedures to assess test-performance characteristics and concordance between gold-standard and kit results are provided below ( Supplementary Data , available as Supplementary data at IJE online).

Statistical analysis

Our estimation of the prevalence of COVID-19 proceeded in three steps. First, we report the raw frequencies of positive tests as a proportion of the final sample size. Second, we report the estimated sample prevalence, adjusted for test performance characteristics. Because SARS-CoV-2 lateral-flow antibody assays are relatively new, we gathered all available information on test performance characteristics (sensitivity and specificity), with a focus on test specificity, which can be of paramount importance when prevalence is not high. We use an estimate of test sensitivity and specificity based on pooling all information available to us. Details of each sample, including test-kit agreement numbers, specimen type and available information on data provenance, are provided in the Supplementary Data , available as Supplementary data at IJE online.

Third, we report the weighted prevalence after weighting for the zip code, sex, age (using four age categories: 0–19, 20–39, 40–69 and 70+) and race/ethnicity (non-Hispanic White, Asian, Hispanic and other) distributions of Santa Clara County (as measured in the 2018 American Community Survey). We use weights obtained through iterative proportional fitting, or raking. 11 Raking generates weights by iteratively adjusting the marginal weights of each population control variable (e.g. sex) until convergence is achieved on all control variables. We match Santa Clara County demographics by zip, sex, age and race.

We use a bootstrap procedure to estimate confidence bounds for the unweighted and weighted prevalence, while accounting for sampling error and propagating the uncertainty in the sensitivity and specificity. We account for clustering of test results within families by drawing household clusters in the bootstrap samples. We use the basic percentile of the bootstrap distribution to construct confidence intervals. 12 This procedure assumes that the community sample, negative control sample and positive control sample are drawn independently. More details on our bootstrap procedures are in the Supplementary Data , available as Supplementary data at IJE online.

Public involvement

Multiple stakeholders in Santa Clara County, including the department of public health, members of the board of supervisors, county parks and multiple community members, were engaged in the design and execution of the study.

The test kit used in this study (Premier Biotech, Minneapolis, MN) was tested prior to field deployment. In all, we collected information on 3404 specimens from 14 sample sets used for assessing the specificity of this particular kit and on 187 specimens from 4 sample sets used for assessing sensitivity. These tests were performed by the test kit manufacturer, US and Chinese regulatory agencies, as well as independent labs. Additional details on the sample sets and the pooling approaches used to combine the estimates are provided in the Supplementary Data , available as Supplementary data at IJE online. The estimated specificity using data from all the samples was 99.53% [95% confidence interval (CI) 99.24–99.73%] and sensitivity was 85.56% (95% CI 79.69–90.26%).

Our study included 3439 individuals who registered for the study and arrived at testing sites. We excluded observations of individuals who could not be tested (e.g. unable to obtain blood or blood clotted, N  = 49), whose test results could not be used (e.g. if an incorrect participant ID was recorded onsite, N  = 32), who did not reside in Santa Clara County ( N  = 29) and who had invalid test results (no control band, N  = 1). This yielded an analytic sample of 3328 individuals with complete records including survey registration, attendance at a test site for specimen collection and lab results ( Figure 1 ). The sample distribution meaningfully deviated from that of the Santa Clara County population along several dimensions: sex (63% in sample was female, 50% in county), race (8% of the sample was Hispanic, 26% in the county; 19% of the sample was Asian, 28% in the county) and zip distribution ( Supplementary Figure 1 , available as Supplementary data at IJE online). Table 1 includes demographic characteristics of our unweighted sample, the weighted sample and Santa Clara County. 13 Supplementary Figure 1 , available as Supplementary data at IJE online, shows the geographical zip-code distribution of study participants in the county (counts and density per 1000 population).

Flow diagram of participants who filled out the survey and registered, visited a site for testing and were associated with a tested specimen. We were able to associate 3328 individuals with complete survey, site and lab-result data. ‘Individuals with completed registrations’ refers to individuals who completed the initial online survey and were able to select a test site and time. ‘No-shows’ refers to participants who filled out the survey and obtained a site registration but for whom we do not have a record of attendance onsite. ‘Unverifiable IDs’ refers to records from the site data with duplicate participant identifications (IDs) for which we cannot verify which individual attended the site (this may be due to participants bringing incorrect IDs and/or technical errors in the REDCap ID assignment process). ‘Samples not collected or not tested’ includes at least 10 individuals who visited the site but did not consent to participate, as well as several children who may have decided not to have their fingers pricked after completing intake onsite. This also includes specimens that were lost before they could be tested in the lab. ‘Unusable or unmatched survey data’ includes individuals with invalid zip codes, participant IDs from the lab results that could not be matched back to the survey responses and participants who withdrew from the study. Unmatched participant IDs may be due to participants stating an incorrect participant ID at the test site or site data collectors incorrectly recording stated participant IDs. ‘Invalid lab result’ refers to one test for which the on-board control failed and the lab result could not be correctly interpreted.

Sample characteristics relative to Santa Clara County population estimates from the 2018 American Community Survey

Table 2 shows that the frequency of positivity in our unweighted sample was similar between men and women, was highest among Hispanic participants and ranged between 1.4% and 1.9% across ages. Positivity among those who reported recent loss of taste in the past 2 weeks ( n  = 59) was ∼22%.

Univariate frequencies of positivity along demographic and clinical features

17 people did not indicate race.

The total number of positive cases by either IgG or IgM in our unadjusted sample was 50—a crude prevalence rate of 1.50% (exact binomial 95% CI 1.11–1.98%; Table 3 ). Accounting for test sensitivity and specificity and sampling error, our point estimate of the unweighted population prevalence was 1.22% (bootstrap 95% CI 0.66–1.79%). After adjusting for test performance and weighting our sample to approximate Santa Clara County demographics by zip, race, age and sex, the prevalence was 2.76% (95% CI 1.32 – 4.22%). The increase in prevalence after weighting is primarily driven by relatively higher prevalence among Hispanic participants residing in under-sampled zip codes of the county. This distribution of higher prevalence in those groups corresponds closely with the distribution of confirmed cases in Santa Clara County in late March: a disproportionate number of cases were experienced by Hispanic populations residing in the eastern portion of Santa Clara County. 14

Prevalence estimation in Santa Clara County

We report the prevalence and uncertainty bounds of estimates from unadjusted frequency counts, estimates adjusted for test-performance characteristics and estimates adjusted for test-performance characteristics and weighted by zip code, sex, age and race/ethnicity. For adjusted prevalences, we estimate the point estimate and uncertainty using the bootstrap as described in the Methods and Supplementary Data , available as Supplementary data at IJE online.

We can use our prevalence estimates to approximate the infection fatality rate from COVID-19 in Santa Clara County. Our prevalence estimate of 2.76% applied to Santa Clara County’s population implies a little over 53 000 infections (95% CI 26 000–82 000). Since the development of antibodies takes ∼7 days from the time of infection, that estimate represents the cumulative incidence of infections in Santa Clara County up to 27 March 2020, 1 week before the first day of testing. We then examine the cumulative COVID-19-associated deaths in Santa Clara County 2, 3 (our preferred) and 4 weeks after 27 March, which allows us to estimate the range of the infection fatality rate given a lag of 2–4 weeks from infection to death. 15 , 16 The number of people who died with COVID-19 in Santa Clara County by 11 April (2 weeks), 18 April (3 weeks) and 25 April (4 weeks) was 65, 90 and 106, respectively ( Supplementary Figure 2 , available as Supplementary data at IJE online, shows the time trends of cases and deaths in Santa Clara County around the time of the study). 17 These estimates of deaths then correspond to an infection fatality rate of 0.12% (65/53 000), 0.17% (90/53 000; preferred) and 0.2% (106/53 000). Finally, we estimated the infection fatality rate within our four age strata (0–19, 20–39, 40–69 and 70+) using the age-specific portion of deaths in Santa Clara County and the implied number of infections from our study and the county demographics. Our data are limited to calculate with accuracy the infection fatality rate in age strata, but they suggest vast differences in fatality risk ( Supplementary Table 4 , available as Supplementary data at IJE online).

After adjusting for test-performance characteristics and weighting for county demographics, we estimate that the seroprevalence of antibodies to SARS-CoV-2 in Santa Clara County in late March was 2.76%, with uncertainty bounds from 1.32% to 4.22%.

The most important implication of these findings is that, early in the pandemic, the number of infections was much greater than the reported number of cases. Using the weighted estimates, our data imply that, by 27 March (7 days prior to our survey), ∼53 000 (95% CI 26 000–82 000) people had been infected in Santa Clara County. The reported number of confirmed positive cases in the county on 27 March was 948 (~1,200 on the days of the study)—56-fold lower than the number of infections predicted by this study. 14 This infection-to-case ratio, also referred to as an under-ascertainment rate, was meaningfully higher than other estimates at the time. 18 , 19 This under-ascertainment rate is a fundamental parameter of many projection and epidemiologic models, and was used as a calibration target for understanding epidemic stage and calculating fatality rates. 20 , 21 The under-ascertainment for COVID-19 is likely due to a combination of reliance on PCR for case identification, which misses convalescent cases; early spread in the absence of systematic testing; and asymptomatic or lightly symptomatic infections that go undetected.

The estimated infection fatality rate of 0.17% is based on the assumption that the prevalence in our study reflects the situation in Santa Clara 7 days prior to the study. If antibodies take longer to appear, or if the average duration from case identification to death is <3 weeks, then the prevalence rate at the time of the survey was higher and the infection fatality rate would be lower. On the other hand, if deaths from COVID-19 are under-reported, then the fatality rate estimates would increase. Our prevalence and fatality rate estimates can be used to update existing models, given the large upwards revision of under-ascertainment.

Whereas our weighted-prevalence estimate of 2.76% is indicative of the situation in Santa Clara County as of late March, other areas are likely to have different seroprevalence estimates based on population demographics, effective contact rates and social-distancing policies. The infection fatality rate in different locations also varies and may be substantially higher in places where the hospitals were overwhelmed (e.g. New York City or Bergamo) or where infections are concentrated among vulnerable individuals (e.g. nursing home residents). 22–24 For example, in many European countries, 42–57% of deaths occurred in nursing homes and preliminary estimates for the USA are approaching the same range. 25 , 26 infection fatality rate estimates may be substantially higher in such settings.

Our prevalence estimate also suggests that, at this time, the large majority of the population in Santa Clara County remains without IgM or IgG antibodies to SARS-CoV-2. However, repeated serologic testing in different geographies, spaced a few weeks apart, is needed to evaluate the extent of infection spread over time.

This study has several limitations. The primary limitation concerns sample selection biases. Our sample may be enriched with COVID-19 participants by selecting for individuals with a belief or curiosity concerning past infection. We discuss further and attempt to quantify the potential impact of this bias in the Supplementary Data , available as Supplementary data at IJE online (Section S4). Notably, we find that, under the most penalizing scenario of selection, the population-prevalence estimate changes from 2.76% to 2.11% ( Supplementary Table 3 , available as Supplementary data at IJE online). Our study may also have selected for groups of people more likely to skew our sample against COVID-19 participants. For example, our sample strategy selected for members of Santa Clara County with ready access to Facebook who viewed our advertisement early after the registration opened. Our sample ended up with an over-representation of White women between the ages of 40 and 70 years, and an under-representation of Hispanic and Asian populations, relative to our community. Those imbalances were partly addressed by weighting our sample by zip code, race, age and sex to match the county. Our survey also selected for members of the population who were able to spare the time to drive to the testing site, which may have skewed our sample against essential workers. Our study was also limited in that it could not ascertain the representativeness of SARS-CoV-2 antibodies in populations with possibly high prevalence, such as homeless populations and those in nursing homes. The overall direction and magnitude of these selection effects are hard to fully bound, and our estimates reflect the prevalence in our sample, weighted to match county demographics.

Another potential limitation is that, with relatively low prevalence estimates, the precision of seroprevalence estimates depends on the performance of the serology tests for SARS-CoV-2 antibodies. Our adjusted results depend on the current estimates of specificity and sensitivity of these tests. We estimated the specificity and sensitivity based on pooled estimates from several independent assessments that use combined IgG and IgM to identify positive-antibody presence. Lower test specificity or greater uncertainty in the test performance could, in principle, meaningfully change the study’s conclusions. The performance of the test kit used in this study has been studied extensively in comparison to other SARS-CoV-2-antibody tests, including for use with capillary blood and in comparison with tests that require venipuncture ( Supplementary Data Section 2, available as Supplementary data at IJE online).

Our infection fatality rate could be biased downward if the number of COVID-19 deaths in the county has been substantially undercounted. This has some plausibility given that some early COVID-19 deaths may have gone undiagnosed due to limited testing. However, many deaths early in the spring of 2020 that were suspected of being due to COVID-19 were retrospectively evaluated, and those in which COVID-19 was implicated in the death were added to the official tally, reducing the effect of undercounting. We note that, even if deaths are 50% higher than the recorded deaths, this would still imply an infection fatality rate of 0.18–0.30%.

Over 100 teams worldwide have tested population samples for SARS-CoV-2 antibodies, with findings consistent with a large under-ascertainment of SARS-CoV-2 infections. Our study was one of the earliest to be done and the large under-ascertainment was partly driven by the limited testing in the early phases of the pandemic. Large under-ascertainment (up to several hundred-fold) was seen in other early surveys, whereas the extent of under-ascertainment decreased as more testing was being performed in most locations. 27 , 28 An early serosurvey in Los Angeles County, California on 10–11 April estimates a seroprevalence of 4.65%. 29 Our data from Santa Clara County suggest that the spread of the infection is similar to other moderately affected areas such as Los Angeles in early spring. Documented infections remained low in Santa Clara County until early summer. Santa Clara was part of a large seroprevalence survey among dialysis patients in the USA. 30 In that study, conducted in the first week of July 2020, the estimated seroprevalence in Santa Clara County was 4.1%, and this corresponded to an infection fatality rate of ∼0.2%, similar to our estimate.

We conclude that, based on seroprevalence sampling of a large regional population, the best estimates for the prevalence of SARS-CoV-2 antibodies in Santa Clara County were 2.76% by late March (95% CI 1.32–4.22%), with large variability by race/ethnicity. This prevalence is far smaller than the theoretical final size of the epidemic 31 and suggests that, in late March, a large majority of the population did not have IgG or IgM antibodies to the virus. Our study offers valuable data for an early period of the pandemic, when PCR testing was limited, and these data can be used as a baseline for comparison against subsequent seroprevalence studies to estimate the evolution of both seroprevalence and infection fatality rates over time. Our prevalence and infection fatality rate estimates do not advocate for or refute the usefulness of any non-pharmaceutical interventions. Instead, these new data reduce uncertainty around the size of the population that has been infected. This allows better monitoring of interventions and reducing uncertainty about the state of the epidemic also may carry intrinsic public benefits. It is important to note that the under-ascertainment of infections in Santa Clara may change over time (e.g. depending on greater availability of testing) and the under-ascertainment rate may be different in other locations. Improved test accuracy and larger sample sizes with random sampling can further reduce uncertainty in estimates. Our work demonstrates the feasibility of seroprevalence surveys of population samples to inform our understanding of this pandemic’s progression, project estimates of community vulnerability and monitor infection fatality rates in different populations over time.

Supplementary data are available at IJE online.

E.B., N.S. and J.B. conceived of the project and were involved in every aspect of the project; B.M., S.S. and J.T. were involved in protocol development and in every aspect of the survey execution; J.P.A.I. conceptualized the data collection and analysis; R.B.D., C.L., Z.W., R.S.W. and A.B. were site directors and led critical components of the data collection and interpretation; T.K. and D.E. were instrumental in assessing, procuring and fielding the test kits; R.G. led recruitment efforts and was involved in data analysis. All authors were critically involved in interpretation of the data, drafting and revising of the manuscript.

This work was supported by gift support from the Stanford COVID-19 Seroprevalence Studies Fund. The funders had no role in the design and conduct of the study, nor in the decision to prepare and submit the manuscript for publication.

The authors acknowledge the support of the Santa Clara County Department of Public Health, the Santa Clara County community, all study participants and the many volunteers and staff without whom this study could not have been accomplished in the midst of the COVID-19 crisis. We would also like to acknowledge the following individuals for critical comments and contributions that improved the data and the paper: David Allison, Manisha Desai, Liran Einav, Julia Gross, Emilia Ling, Tom MaCurdy, Charles McCulloch, Ben Moran, Barry Nalebuff, Richard Olshen, Ken Shotts and Frank Wolak. The Institutional Review Board at Stanford University approved the study prior to recruitment (Protocol # IRB-55702). Sharing of the primary data is restricted under a human-subjects-protection agreement. Sharing of de-identified data may be available upon request and review by the authors.

Conflict of interest

None declared. Of note, test kits were purchased from Premier Biotech for this study, and none of the authors has a relationship to the test manufacturer (beyond purchasing the tests).

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