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Although the vast majority of confirmed cases of COVID-19 are in low- and middle-income countries, there are relatively few published studies on the epidemiology of SARS-CoV-2 in these countries. The few there are focus on disease prevalence in urban areas. We conducted state-wide surveillance for COVID-19, in both rural and urban areas of Karnataka between June 15-August 29, 2020. We tested for both viral RNA and antibodies targeting the receptor binding domain (RBD). Adjusted seroprevalence across Karnataka was 46.7% (95% CI: 43.3-50.0), including 44.1% (95% CI: 40.0-48.2) in rural and 53.8% (95% CI: 48.4-59.2) in urban areas. The proportion of those testing positive on RT-PCR, ranged from 1.5 to 7.7% in rural areas and 4.0 to 10.5% in urban areas, suggesting a rapidly growing epidemic. The relatively high prevalence in rural areas is consistent with the higher level of mobility measured in rural areas, perhaps because of agricultural activity. Overall seroprevalence in the state implies that by August at least 31.5 million residents had been infected by August, nearly an order of magnitude larger than confirmed cases.
RESUMO
ObjectiveEstimate seroprevalence in representative samples from slum and non-slum communities in Mumbai, India, a mega-city in a low or middle-income country and test if prevalence is different in slums. DesignAfter geographically-spaced community sampling of households, one individual per household was tested for IgG antibodies to SARS-CoV-2 N-protein in a two-week interval. SettingSlum and non-slum communities in three wards, one each from the three main zones of Mumbai. ParticipantsIndividuals over age 12 who consent to and have no contraindications to venipuncture were eligible. 6,904 participants (4,202 from slums and 2,702 from non-slums) were tested. Main outcome measuresThe primary outcomes were the positive test rate for IgG antibodies to the SARS-CoV-2 N-protein by demographic group (age and gender) and location (slums and non-slums). The secondary outcome is seroprevalence at slum and non-slum levels. Sera was tested via chemiluminescence (CLIA) using Abbott Diagnostics ArchitectTM N-protein based test. Seroprevalence was calculated using weights to match the population distribution by age and gender and accounting for imperfect sensitivity and specificity of the test. ResultsThe positive test rate was 54.1% (95% CI: 52.7 to 55.6) and 16.1% (95% CI: 14.9 to 17.4) in slums and non-slums, respectively, a difference of 38 percentage points (P < 0.001). Accounting for imperfect accuracy of tests (e.g., sensitivity, 0.90; specificity 1.00), seroprevalence was as high as 58.4% (95% CI: 56.8 to 59.9) and 17.3% (95% CI: 16 to 18.7) in slums and non-slums, respectively. ConclusionsThe high seroprevalence in slums implies a moderate infection fatality rate. The stark difference in seroprevalence across slums and non-slums has implications for the efficacy of social distancing, the level of herd immunity, and equity. It underlines the importance of geographic specificity and urban structure in modeling SARS-CoV-2.
RESUMO
India has reported the fourth highest number of confirmed SARS-CoV-2 cases worldwide. Because there is little community testing for COVID, this case count is likely an underestimate. When India partially exited from lockdown on May 4, 2020, millions of daily laborers left cities for their rural family homes. RNA testing on a near-random sample of laborers returning to the state of Bihar is used to estimate positive testing rate for COVID across India for a 6-week period immediately following the initial lifting of Indias lockdown. Positive testing rates among returning laborers are only moderately correlated with, and 21% higher than, Indian states official reports, which are not based on random sampling. Higher prevalence among returning laborers may also reflect greater COVID spread in crowded poor communities such as slums.