key: cord-0331505-b468rh4s authors: Moreira-Soto, A.; Pachamora Diaz, J. M.; Gonzalez-Auza, L.; Merino Merino, X. J.; Schwalb, A.; Drosten, C.; Gotuzzo, E.; Talledo, M.; Arevalo Ramirez, H.; Peralta Delgado, R.; Bocanegra Vargas, S.; Drexler, J. F. title: High SARS-CoV-2 seroprevalence in rural Peru, 2021; a cross-sectional population-based study date: 2021-10-23 journal: nan DOI: 10.1101/2021.10.19.21265219 sha: 30e8637bdf76fce225027f542222e9dd8a31cc0c doc_id: 331505 cord_uid: b468rh4s Latin America has been severely affected by the COVID-19 pandemic. The COVID-19 burden in rural settings in Latin America is unclear. We performed a cross-sectional, population-based, random-selection SARS-CoV-2 serological study during March 2021 in the rural population of San Martin region, northern Peru. The study enrolled 563 persons from 288 houses across 10 provinces, reaching 0.19% of the total rural population of San Martin. Screening for SARS-CoV-2 IgG antibodies was done using a chemiluminescence immunoassay (CLIA) and reactive sera were confirmed using a SARS-CoV-2 surrogate virus neutralization test (sVNT). Validation using pre-pandemic sera from two regions of Peru showed false-positive results in the CLIA (23/84 sera; 27%), but not in the sVNT, highlighting the pitfalls of SARS-CoV-2 antibody testing in tropical regions and the high specificity of the two-step testing algorithm. An overall 59.0% seroprevalence (95% CI: 55-63%) corroborated intense SARS-CoV-2 spread in San Martin. Seroprevalence rates between the 10 provinces varied from 41.3-74.0% (95% CI: 30-84). Higher seroprevalence was neither associated with population size, population density, surface area, mean altitude or poverty index in spearman correlations. Seroprevalence and reported incidence diverged substantially between provinces, suggesting regional biases of COVID-19 surveillance data. Potentially, limited healthcare access due to environmental, geographic, economic, and cultural factors, might lead to undetected infections in rural populations. Additionally, test avoidance to evade mandatory quarantine might affect rural regions more than urban regions. Serologic diagnostics should be pursued in resource-limited settings to inform country-level surveillance, vaccination strategies and support control measures for COVID-19. index in spearman correlations. Seroprevalence and reported incidence diverged substantially 42 between provinces, suggesting regional biases of COVID-19 surveillance data. Potentially, limited 43 healthcare access due to environmental, geographic, economic, and cultural factors, might lead to 44 undetected infections in rural populations. Additionally, test avoidance to evade mandatory 45 quarantine might affect rural regions more than urban regions. Serologic diagnostics should be 46 pursued in resource-limited settings to inform country-level surveillance, vaccination strategies 47 and support control measures for COVID-19. in rural settings is scarce. Using a representative population-based seroprevalence study, we 54 detected a high seroprevalence in rural populations in San Martin, northern Peru in 2021, reaching 55 41 to 74 %. However, seroprevalence and reported incidence diverged substantially between 56 regions, suggesting either limited healthcare access or test avoidance due to mandatory quarantine. 57 Our results suggest that rural populations are highly affected by SARS-CoV-2 even though they 58 are socio-demographically distinct from urban populations, and that highly specific serological 59 diagnostics should be performed in resource-limited settings to support public-health strategies of 60 COVID-19 surveillance and control. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) dispersion and low population density of these communities, in addition to regular travelling to 82 urban centers for commerce, might contribute to a differential dispersion of SARS-CoV-2 83 compared to urban settings. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 23, 2021. ; https://doi.org/10. 1101 Our study focused on the rural region of San Martin, Peru (total population: 899, 648 density: 86 18/km 2 ). The San Martin department (51,000 km 2 ) is located in the north of Peru ( Figure 1A) . 87 The department encompasses diverse ecosystems such as valleys, Andes mountains and Amazon 88 rainforests, reaching altitudes from 190 to 3,080 m. (Figure 1A ) and comprises ≤5% of Peruvian performed using a kish grid (7). A 10 mL blood sample was taken after the informed consent was 106 signed by the person or the caretaker if the person had ≤18 years of age. We obtained samples 107 from 563 persons visiting 288 houses in the 10 provinces, comprising 0.19% of the total rural 108 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 23, 2021. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 23, 2021. ; https://doi.org/10.1101/2021.10.19.21265219 doi: medRxiv preprint sVNT which can be attributed to differential sensitivity of the tests. (Figure 1C and Annex Figure 132 2). None of the CLIA-positive pre-pandemic samples yielded positive results in the sVNT. These 133 results corroborate that unspecific reactivity of serological tests in tropical areas must be carefully 134 evaluated (10) and highlight robustness of our serological testing algorithm. Therefore, only 135 samples yielding positive results in both serological assays were considered for further analyses in 136 our study to guarantee specificity. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 23, 2021. ; https://doi.org/10.1101/2021.10.19.21265219 doi: medRxiv preprint (Figure 2B) , showed no correlation with our serological data although cumulatively measuring 154 similar time spans, encompassing early 2020-March 2021 (rs=-0.14; p=0.68) (Figure 1B and 155 Figure 2D ). On the one hand, overall higher seroprevalence than reported cases is consistent with 156 high numbers of undetected asymptomatic COVID-19 cases (12). On the other hand, we detected 157 two provinces with a relatively higher divergence of seroprevalence compared to incidence data 158 (Mariscal Caceres and Bellavista, Figure 2A, 2B and 2D) . Hypothetically, individuals in those 159 provinces may lack healthcare access due to environmental, geographic, economic, and cultural CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 23, 2021. ; https://doi.org/10.1101 https://doi.org/10. /2021 Irrespective of the underlying reasons leading to the overall high seroprevalence detected, our data 177 suggest a large number of undiagnosed COVID-19 cases potentially challenging test-trace-isolate 178 interventions in the region (19) . Previous studies in the USA have stressed that rural regions are 179 particularly vulnerable to COVID-19, leading to higher mortality rates in rural than in urban We thank the DIRESA workers that aided in the sample collection and all study participants. We CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 23, 2021. ; https://doi.org/10.1101/2021.10.19.21265219 doi: medRxiv preprint The authors report no conflicts of interest. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 23, 2021. ; https://doi.org/10.1101 .21265219 doi: medRxiv preprint . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted October 23, 2021. ; https://doi.org/10.1101/2021.10.19.21265219 doi: medRxiv preprint 303 Annex Figure 3 . Spearman's rank correlation test of seroprevalence and population, population 304 density, surface area, poverty index and altitude. Rs= Spearman's correlation coefficient. Epidemiological data taken from: http://sial.minam.gob.pe/sanmartin/indicador/850 , 306 https://www.citypopulation.de/en/peru/sanmartin/admin/ 307 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. 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