Reconciling epidemiological models with misclassified case-counts for SARS-CoV-2 with seroprevalence surveys: A case study in Delhi, India

Abstract

Underreporting of COVID-19 cases and deaths is a hindrance to correctly modeling and monitoring the pandemic. This is primarily due to limited testing, lack of reporting infrastructure and a large number of asymptomatic infections. In addition, diagnostic tests (RT-PCR tests for detecting current infection) and serological antibody tests for IgG (to assess past infections) are imperfect. In particular, the diagnostic tests have a high false negative rate. Epidemiologic models with a latent compartment for unascertained infections like the Susceptible-Exposed-Infected-Removed (SEIR) models can provide predictions for unreported cases and deaths under certain assumptions. Typically, the number of unascertained cases is unobserved and thus we cannot validate these estimates for a real study except for simulation studies. Population-based seroprevalence studies can provide a rough estimate of the total number of infections and help us check epidemiologic model projections. In this paper, we develop a method to account for high false negative rates in RT-PCR in an extension to the classic SEIR model. We apply this method to Delhi, the national capital region of India, with a population of 19.8 million and a COVID-19 hotspot of the country, obtaining estimates of underreporting factor for cases at 34-53 times and that for deaths at 8-13 times. Based on a recently released serological survey for Delhi with an estimated 22.86% seroprevalence, we compute adjusted estimates of the true number of infections reported by the survey (after accounting for misclassification of the antibody test results) which is largely consistent with the model outputs, yielding an underreporting factor for cases from 30-42. Together with the model and the serosurvey, this implies approximately 96-98% cases in Delhi remained unreported and whereas only 109,140 cases were reported on July 10, the true number of infections varied somewhere between 4.4-4.6 million across different estimates. While repeated serological monitoring is resource intensive, model-based adjustments, run with the most up to date data, can provide a viable option to keep track of the unreported cases and deaths and gauge the true extent of transmission of this insidious virus.

SARS-CoV-2 prevalence associated to low socioeconomic status and overcrowding in an LMIC megacity: A population-based seroepidemiological survey in Lima, Peru

“…Lima had a higher estimate than the most populated cities in Brazil, including Rio de Janeiro (7¢5%) and São Paulo (2¢3%) [28]. Outside Latin America, the closest estimate to Lima was Delhi in India (23%) [29]. Furthermore, the seroprevalence was higher than London in England (13%) [30] and Madrid in Spain (11¢3%), [18] where the capital city had higher estimates compared to the rest of the country.…”

Seroprevalence of anti-SARS-CoV-2 antibodies in residents of Karachi—challenges in acquiring herd immunity for COVID 19

“…22% for Iran and 22–33% for India. 15 , 16 Very little was found in the literature on the question of increased seropositivity in Asian countries. However, there could be multiple reasons for this interesting finding and some of the points need more clarification and further research.…”

Decoding India's Low Covid-19 Case Fatality rate

“…Our fatality rate is much less than most other countries." 3 To move from a low CFR to an unqualified commendation of deliberate policy-induced containment of the disease might (to put it mildly) overlook certain crucial aspects of demographic detail. That the age distribution within a country will influence the CFR is widely known, with "younger countries" exhibiting lower CFRs simply on account of lower death rates among younger age groups.…”

“…Bhramar Mukherjee, in private communication, suggests a nationwide underreporting factor of the order of 15-25, implying a current infected population of 30-50 million. See Bhattacharyya et al[2] for more details on the calculation of the underrporting factor for India.2 There are many reasons why the absolute values of CFRs have no obvious and natural meaning, some of which will play an unavoidable role in this paper 3. The Hindustan Times, July 26, 2020; https://www.hindustantimes.com/india-news/pm-narendra-modi-s -67th-mann-ki-baat-address-to-nation-highlights/story-bXhnWiU1WElwNLFp0mpWRJ.html.…”