Regional Difference in Seroprevalence of SARS-CoV-2 in Tokyo: Results from the community point-of-care antibody testing

Takita, Morihito; Matsumura, Tomoko; Yamamoto, Kana; Yamashita, Erika; Hosoda, Kazutaka; Hamaki, Tamae; Kusumi, Eiji

doi:10.1101/2020.06.03.20121020

Cited by 9 publications

(10 citation statements)

References 3 publications

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“…(30) Similarly, the seroprevalence reported from two community clinics in Tokyo was 3.83% (95% CI 2.76-5.16). (31)While others have reported a higher seroprevalence among the elderly, our study did not find any difference between the age groups with respect to their seropositivity. Thus, the evidence till date is showing significant regional as well as time dependant variations in findings of serosurveys to assess the exposure to SARS-CoV-2.Most serosurveys, including ours have reported a large majority of infections to be asymptomatic in nature.…”

Section: Discussioncontrasting

confidence: 91%

Findings from serological surveys (in August 2020) to assess the exposure of adult population to SARS Cov-2 infection in three cities of Odisha, India

Kshatri

Bhattacharya

Kanungo

et al. 2020

Preprint

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Background: There is always an uncertainty of epidemiological , serological infectivity and virulence of the emerging novel coronavirus. Antibody test can be used for assessing whether immunity has developed in the infected person after 5-7 days of illness and understand cumulative exposure levels to the infection, make inferences on the actual burden of infection, its geographical spread, effect on specific demographic/risk groups, gaps in testing and infection fatality rates. Objective- To estimate and compare the sero-prevalence, hidden prevalence and determine the demographic risk factors associated with SARS-CoV-2 infection among adults in three largest cities of Odisha, India. Methodology: This was a population based cross sectional serological survey carried out in August 2020 in the three largest cities of the state of Odisha. Sample size per city was estimated to be 1500 and participants were enrolled from the community using multi-stage random sampling from 25 clusters from each city. Data was collected using ODK based tools by household visits and 3-4 ml of blood samples were collected after informed consent. Samples were transported to testing lab where Serum was separated and tested for anti- SARS CoV-2 antibodies using automated CLIA platform. Statistical analysis was done using R-software packages. Results: A total of 4146 participants from the 3 cities of Bhubaneswar (BBS), Berhampur (BAM) and Rourkela (RKL) participated. A total of 5635 households were approached and the average non response rate in the community was 17.4%. The gender weighted seroprevalence across the three cities was 20.78% (95% CI: 19.56%-22.05%). Seroprevalence was highest in BAM at 31.14% (95% CI: 28.69-33.66%) followed by 24.59% (95% CI: 22.39-26.88%) in RKL and 5.24% (95% CI: 4.10-6.58%) in BBS. While females reported a higher seroprevalence (22.8%) as compared to males (18.8%), there was no significant difference in seroprevalence across age groups. A majority of the seropositive participants were asymptomatic (93.87%). Among those who reported symptoms, the most common symptom was fever (68.89%) followed by cough (46.06%) and myalgia (32.67%). The case to infection ratio on the date of serosurvey was 1: 6.6 in BBS, 1:61 in BAM and 1:29.8 in RKL. Conclusion: The study found a high seroprevalence against COVID-19 in urban Odisha as well as high numbers of asymptomatic infections.

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Section: Discussioncontrasting

confidence: 91%

Findings from serological surveys (in August 2020) to assess the exposure of adult population to SARS Cov-2 infection in three cities of Odisha, India

Kshatri

Bhattacharya

Kanungo

et al. 2020

Preprint

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“…Both undercounting and overcounting of COVID-19 deaths may be a caveat in different locations and this is difficult to settle in the absence of very careful scrutiny of medical records and autopsies. The Tokyo data, 29 nevertheless, also show similarly very low IFR. Moreover, evaluation of all-cause mortality in Japan has shown no excess deaths during the pandemic, consistent with the possibility that somehow the Japanese population was spared.…”

Section: Discussionmentioning

confidence: 79%

The infection fatality rate of COVID-19 inferred from seroprevalence data

Ioannidis

2020

Preprint

178

182

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Objective To estimate the infection fatality rate of coronavirus disease 2019 (COVID-19) from data of seroprevalence studies. Methods Population studies with sample size of at least 500 and published as peer-reviewed papers or preprints as of July 11, 2020 were retrieved from PubMed, preprint servers, and communications with experts. Studies on blood donors were included, but studies on healthcare workers were excluded. The studies were assessed for design features and seroprevalence estimates. Infection fatality rate was estimated from each study dividing the number of COVID-19 deaths at a relevant time point by the number of estimated people infected in each relevant region. Correction was also attempted accounting for the types of antibodies assessed. Secondarily, results from national studies were also examined from preliminary press releases and reports whenever a country had no other data presented in full papers of preprints. Results 36 studies (43 estimates) were identified with usable data to enter into calculations and another 7 preliminary national estimates were also considered for a total of 50 estimates. Seroprevalence estimates ranged from 0.222% to 47%. Infection fatality rates ranged from 0.00% to 1.63% and corrected values ranged from 0.00% to 1.31%. Across 32 different locations, the median infection fatality rate was 0.27% (corrected 0.24%). Most studies were done in pandemic epicenters with high death tolls. Median corrected IFR was 0.10% in locations with COVID-19 population mortality rate less than the global average (<73 deaths per million as of July 12, 2020), 0.27% in locations with 73-500 COVID-19 deaths per million, and 0.90% in locations exceeding 500 COVID-19 deaths per million. Among people <70 years old, infection fatality rates ranged from 0.00% to 0.57% with median of 0.05% across the different locations (corrected median of 0.04%). Conclusions The infection fatality rate of COVID-19 can vary substantially across different locations and this may reflect differences in population age structure and case-mix of infected and deceased patients as well as multiple other factors. Estimates of infection fatality rates inferred from seroprevalence studies tend to be much lower than original speculations made in the early days of the pandemic.

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“…To explore whether this is a universal trend, we perform the same analysis for nine different locations at which COVID-19 antibody prevalence was measured in a representative sample of the population, Heinsberg (NRW, Germany) [71], Ada County (ID, USA) [4], New York City (NY, USA) [58], Santa Clara County (CA, USA) [3], Denmark [14], Geneva Canton (Switzerland) [72], Netherlands [67], Rio Grande do Sul (Brasil) [66], and Belgium [64]. The fraction of the symptomatic population in these nine locations is v s = 20.00%, 7.90%, 5.76%, 1.77%, 6.95%, 10.34%, 17.31%, 8.10%, and 10.21% respectively, broadly representing the range of reported symptomatic versus estimated total cases worldwide [3, 4, 9, 12, 14, 18, 24, 32, 34, 46, 58, 59, 64, 65, 68, 66, 67, 71, 72, 74, 76, 78, 81]. Of the nine locations we analyzed here, Heinsberg tested IgG and IgA, Ada County tested IgG, New York City tested IgG, Santa Clara County tested IgG and IgM, Denmark tested IgG and IgM, Geneva tested IgG, the Netherlands tested IgG, IgM and IgA, Rio Grande do Sul tested IgG and IgM, and Belgium tested IgG.…”

Section: Discussionmentioning

confidence: 99%

“…In this manuscript, we collectively use the term “asymptomatic” for undetected individuals who either have mild symptoms that are not directly associated with COVID-19 or display no symptoms at all. Recent antibody seroprevalence studies suggests that the number of asymptomatic COVID-19 cases outnumbers the symptomatic cases by an order of magnitude or more [3, 4, 9, 12, 14, 18, 24, 32, 34, 46, 58, 59, 64, 65, 68, 66, 67, 71, 72, 74, 76, 78, 81]. Estimating the prevalence and contagiousness of these asymptomatic cases is critical since it will change our understanding of the overall dimension and the pandemic potential of COVID-19 [16].…”

Section: Motivationmentioning

confidence: 99%

Visualizing the invisible: The effect of asymptomatic transmission on the outbreak dynamics of COVID-19

Peirlinck¹,

Linka²,

Costabal

et al. 2020

Preprint

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Understanding the outbreak dynamics of the COVID-19 pandemic has important implications for successful containment and mitigation strategies. Recent studies suggest that the population prevalence of SARS-CoV-2 antibodies, a proxy for the number of asymptomatic cases, could be an order of magnitude larger than expected from the number of reported symptomatic cases. Knowing the precise prevalence and contagiousness of asymptomatic transmission is critical to estimate the overall dimension and pandemic potential of COVID-19. However, at this stage, the effect of the asymptomatic population, its size, and its outbreak dynamics remain largely unknown. Here we use reported symptomatic case data in conjunction with antibody seroprevalence studies, a mathematical epidemiology model, and a Bayesian framework to infer the epidemiological characteristics of COVID-19. Our model computes, in real time, the time-varying contact rate of the outbreak, and projects the temporal evolution and credible intervals of the effective reproduction number and the symptomatic, asymptomatic, and recovered populations. Our study quantifies the sensitivity of the outbreak dynamics of COVID-19 to three parameters: the effective reproduction number, the ratio between the symptomatic and asymptomatic populations, and the infectious periods of both groups For nine distinct locations, our model estimates the fraction of the population that has been infected and recovered by Jun 15, 2020 to 24.15% (95% CI: 20.48%-28.14%) for Heinsberg (NRW, Germany), 2.40% (95% CI: 2.09%-2.76%) for Ada County (ID, USA), 46.19% (95% CI: 45.81%-46.60%) for New York City (NY, USA), 11.26% (95% CI: 7.21%-16.03%) for Santa Clara County (CA, USA), 3.09% (95% CI: 2.27%-4.03%) for Denmark, 12.35% (95% CI: 10.03%-15.18%) for Geneva Canton (Switzerland), 5.24% (95% CI: 4.84%-5.70%) for the Netherlands, 1.53% (95% CI: 0.76%-2.62%) for Rio Grande do Sul (Brazil), and 5.32% (95% CI: 4.77%-5.93%) for Belgium. Our method traces the initial outbreak date in Santa Clara County back to January 20, 2020 (95% CI: December 29, 2019 - February 13, 2020). Our results could significantly change our understanding and management of the COVID-19 pandemic: A large asymptomatic population will make isolation, containment, and tracing of individual cases challenging. Instead, managing community transmission through increasing population awareness, promoting physical distancing, and encouraging behavioral changes could become more relevant.

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Regional Difference in Seroprevalence of SARS-CoV-2 in Tokyo: Results from the community point-of-care antibody testing

Cited by 9 publications

References 3 publications

Findings from serological surveys (in August 2020) to assess the exposure of adult population to SARS Cov-2 infection in three cities of Odisha, India

Findings from serological surveys (in August 2020) to assess the exposure of adult population to SARS Cov-2 infection in three cities of Odisha, India

The infection fatality rate of COVID-19 inferred from seroprevalence data

Visualizing the invisible: The effect of asymptomatic transmission on the outbreak dynamics of COVID-19

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