Estimating COVID-19 Prevalence in the United States: A Sample Selection Model Approach

Abstract

Background Public health efforts to determine population infection rates from coronavirus disease 2019 (COVID-19) have been hampered by limitations in testing capabilities and the large shares of mild and asymptomatic cases. We developed a methodology that corrects observed positive test rates for non-random sampling to estimate population infection rates across U.S. states from March 31 to April 7. Methods We adapted a sample selection model that corrects for non-random testing to estimate population infection rates. The methodology compares how the observed positive case rate vary with changes in the size of the tested population, and applies this gradient to infer total population infection rates. Model identification requires that variation

A novel comprehensive metric to assess effectiveness of COVID-19 testing: Inter-country comparison and association with geography, government, and policy response

“…Despite the parsimoniousness, the results were comparable to more complex modeling approaches [ 44 , 45 ]. The findings suggested that nearly 90% of global infections were unreported in the first four months of the pandemic, which was consistent with previous estimates showing that true number of infections are many times higher than reported cases [ 10 – 12 , 17 ]. In addition, the true number of infections as a percentage of the population has been modeled in various European countries.…”

“…One challenge, though, is the substantial proportion of the infected population that is asymptomatic [ 4 , 8 , 9 ]. Consequently, diagnostic testing has been inadequate to reveal what proportion of the population is infected, with real infections in most countries estimated to be 10 to 15 times, and sometimes even >100 times, higher than the reported number of cases [ 10 – 12 ]. Furthermore, predicating reopening dates on incidence may disincentivize testing, since increased diagnostic testing will inherently uncover more cases and, thus, delay reopening.…”

Prevalence of SARS-CoV-2 antibodies in a large nationwide sample of patients on dialysis in the USA: a cross-sectional study

“…We assumed a nationwide prevalence of SARS-CoV-2 antibody of 5%. 8 , 24 To generate prevalence estimates for patients on dialysis using preselected regional strata with precision within 0·5%, a sample of 27 364 was required ( appendix p 2 ). Based on previous trends, we expected 15% of selected samples to be unavailable in July, 2020, due to death, move to other facilities, or other reasons for missing laboratory data (eg, hospitalisation or non-adherence).…”

A framework for surveillance of emerging pathogens at the human-animal interface: Pigs and coronaviruses as a case study

“… Risk factor Type Rationale Scale Source Caveats Pathogen availability in hosts ( A ) Host density A Pathogen availability – all host species Host density affects dynamics and prevalence of CoVs in each host population County (All data streams) ( Lewis et al, 2017 ) using methods from ( Lewis et al, 2019 ) ( USDA, 2020 ) ( Institute, 2018 ) Wild pigs: density over time is important because densities can fluctuate dramatically due to birth pulses and control efforts. Commercial domestic pigs: Size of farms may not correlate directly to risk due to differences in farm connectivity and biosecurity CoV trends in hosts A Pathogen availability – all host species Historical trends of CoV circulation in hosts could represent hotspots for CoV availability in hosts County (All data streams) ( USDA-APHIS, 2015 ; Bevins et al, 2018 ; Benatia et al, 2020 ) Recent prevalence of specific ‘high-risk’ CoVs would be a more direct risk metric of pathogen availability Climate A Pathogen availability – all host species CoV transmission within host species will be higher in colder climates because CoVs persist longer outside hosts in colder climates providing an additional source of infection within host species (i.e., higher virus availability). County nCLIMGRID ( Vose et al, 2014 ) Relationship of climate and CoV prevalence remains poorly understood, is likely non-linear, and depends on other factors that could modify its effects.…”