Analysis of COVID-19 and Chikungunya Co-circulation between 2019 and 2020 in the community-based cohort of Pau da Lima, Salvador, Brazil Restricted; Files Only

Abstract

An outbreak of chikungunya in the Brazilian state of Bahia leading from 2019 into 2020 created a co- circulation event in which the populace was subjected to both the arbovirus as well as the emerging SARS- CoV-2 global pandemic. A cohort study was being conducted in the Pau da Lima neighborhood to measure arbovirus infection rates which was then adapted to capture data regarding SARS-CoV-2 within the population. The spatial distributions of the SARS-CoV-2 status, chikungunya infection status, and other relevant covariates were mapped, and the simultaneous outbreaks were described. An infection rate of approximately 50% for chikungunya virus and 45% for SARS-CoV-2 was identified between 2019 and 2020, which represents significantly high transmission rates for both infections. Moreover, 42.2% of those with incident chikungunya virus infection in 2020 had positive results for SARS-CoV-2 IgG ELISA compared to 47.3% among those with prevalent chikungunya virus infection in 2019 and 46.8% of those with no chikungunya virus infection. No statistically significant association was identified between chikungunya status and SARS-CoV-2 incidence and there was no evidence of a spatially dependent process affecting the relationship. Together this suggests that the high rates of infections resulting from both viruses were independently co-circulating infections that did not interact, spatially or otherwise. Further research would be required to determine if this pattern is consistent across other populations that experienced simultaneous SARS-CoV-2 and arbovirus outbreaks. 

Table of Contents

1. Introduction 1

   1.1 Chikungunya 1

   1.2 SARS-CoV-2 1

   1.3 Co-circulation 2

   1.4 Motivation 4

2. Methods 4

   2.1 Community Based Cohort Study 4

   2.2 Covariate Distributions 7

   2.3 Regression Models 8

   2.4 Spatial Econometric Models 9

   2.5 Geographically Weighted Regression. 11

3. Results 12

   3.1 Description of Data. 12

   3.2 Distribution Maps. 15

   3.3 Aspatial Models. 18

   3.4 Spatial Econometric Models 19

4. Discussion 20

   4.1 Principal Findings 20

   4.2 Limitations 22

   4.3 Comparisons 23

   4.4 Implications 23

5. References 25

6. Appendices. 27

   6.A Additional Methods 27

   6.B Additional Tables & Figures 29

   6.C Additional Results 32

About this Master's Thesis

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School	Rollins School of Public Health
Department	Epidemiology
Degree	M.P.H.
Submission	Master's Thesis
Language	English
Research Field	Health Sciences, Public Health Health Sciences, Epidemiology
Palabra Clave	COVID-19 Co-circulation Brazil Chikungunya
Committee Chair / Thesis Advisor	Benjamin Lopman, Emory University
Committee Members	Guilherme Ribeiro, Visiting Scholar - ECAS: Environmental Science

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