Measuring the effects of COVID-19-related disruption on dengue transmission in southeast Asia and Latin America: a statistical modelling study - 21/04/22

Doi : 10.1016/S1473-3099(22)00025-1

Yuyang Chen, PhD ^a,^{*
Joint first authors}, Naizhe Li, PhD ^a,^b,^{*
Joint first authors}, José Lourenço, DPhil ^c, Lin Wang, PhD ^d,^e, Bernard Cazelles, ProfPhD ^f,^g, Lu Dong, ProfPhD ^b, Bingying Li, MS ^a, Yang Liu, PhD ^h,ⁱ, Mark Jit, ProfPhD ^h,ⁱ, Nikos I Bosse, MS ^h,ⁱ, Sam Abbott, PhD ^h,ⁱ, Raman Velayudhan, PhD ^k, Annelies Wilder-Smith, ProfPhD ^j,^l, Huaiyu Tian, ProfPhD ^a,^{†
Contributed equally}, ⁎ , Oliver J Brady, DPhil ^h,^i,⁎, ^{†
Contributed equally}
on behalf of the
CMMID COVID-19 Working Group^‡
Members are listed in the Supplementary Material

Simon R Procter, Kerry LM Wong, Joel Hellewell, Nicholas G Davies, Christopher I Jarvis, Ciara V McCarthy, Graham Medley, Sophie R Meakin, Alicia Rosello, Emilie Finch, Rachel Lowe, Carl A B Pearson, Samuel Clifford, Billy J Quilty, Stefan Flasche, Hamish P Gibbs, Lloyd A C Chapman, Katherine E. Atkins, David Hodgson, Rosanna C Barnard, Timothy W Russell, Petra Klepac, Yalda Jafari, Rosalind M Eggo, Paul Mee, Matthew Quaife, Akira Endo, Sebastian Funk, Stéphane Hué, Adam J Kucharski, W John Edmunds, Kathleen O’Reilly, Rachael Pung, C Julian Villabona-Arenas, Amy Gimma, Kaja Abbas, Kiesha Prem, Gwenan M Knight, Fiona Yueqian Sun, William Waites, James D Munday, Mihaly Koltai, Frank G Sandmann, Damien C Tully

^a State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China

^b MOE Key Laboratory for Biodiversity Science and Ecological Engineering, College of Life Sciences, Beijing Normal University, Beijing, China

^c Biosystems and Integrative Sciences Institute, University of Lisbon, Lisbon, Portugal

^d Department of Genetics, University of Cambridge, Cambridge, UK

^e Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France

^f Institut de Biologie de l’École Normale Supérieure UMR8197, Eco-Evolutionary Mathematics, École Normale Supérieure, Paris, France

^g Unité Mixte Internationnale 209, Mathematical and Computational Modeling of Complex Systems, Sorbonne Université, Paris, France

^h Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK

ⁱ Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK

^j Department of Disease Control, London School of Hygiene & Tropical Medicine, London, UK

^k Department of Control of Neglected Tropical Diseases, WHO, Geneva, Switzerland

^l Heidelberg Institute of Global Health, University of Heidelberg, Heidelberg, Germany

^* Correspondence to: Dr Oliver J Brady, Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK Department of Infectious Disease Epidemiology Faculty of Epidemiology and Population Health London School of Hygiene & Tropical Medicine London WC1E 7HT UK ^* Correspondence to: Prof Huaiyu Tian, State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China State Key Laboratory of Remote Sensing Science Center for Global Change and Public Health College of Global Change and Earth System Science Beijing Normal University Beijing 100875 China

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Summary

Background

The COVID-19 pandemic has resulted in unprecedented disruption to society, which indirectly affects infectious disease dynamics. We aimed to assess the effects of COVID-19-related disruption on dengue, a major expanding acute public health threat, in southeast Asia and Latin America.

Methods

We assembled data on monthly dengue incidence from WHO weekly reports, climatic data from ERA5, and population variables from WorldPop for 23 countries between January, 2014 and December, 2019 and fit a Bayesian regression model to explain and predict seasonal and multi-year dengue cycles. We compared model predictions with reported dengue data January to December, 2020, and assessed if deviations from projected incidence since March, 2020 are associated with specific public health and social measures (from the Oxford Coronavirus Government Response Tracer database) or human movement behaviours (as measured by Google mobility reports).

Findings

We found a consistent, prolonged decline in dengue incidence across many dengue-endemic regions that began in March, 2020 (2·28 million cases in 2020 vs 4·08 million cases in 2019; a 44·1% decrease). We found a strong association between COVID-19-related disruption (as measured independently by public health and social measures and human movement behaviours) and reduced dengue risk, even after taking into account other drivers of dengue cycles including climatic and host immunity (relative risk 0·01–0·17, p<0·01). Measures related to the closure of schools and reduced time spent in non-residential areas had the strongest evidence of association with reduced dengue risk, but high collinearity between covariates made specific attribution challenging. Overall, we estimate that 0·72 million (95% CI 0·12–1·47) fewer dengue cases occurred in 2020 potentially attributable to COVID-19-related disruption.

Interpretation

In most countries, COVID-19-related disruption led to historically low dengue incidence in 2020. Continuous monitoring of dengue incidence as COVID-19-related restrictions are relaxed will be important and could give new insights into transmission processes and intervention options.

Funding

National Key Research and Development Program of China and the Medical Research Council.

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Plan

Introduction

Methods

Study design and participants

Climate and population data

Restrictions and human mobility data

Collinearity analysis

Under-reporting analysis

Statistical analysis

Role of the funding source

Results

Discussion

Data sharing

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Vol 22 - N° 5

P. 657-667 - mai 2022 Retour au numéro

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Measuring the effects of COVID-19-related disruption on dengue transmission in southeast Asia and Latin America: a statistical modelling study - 21/04/22

Summary

Background

Methods

Findings

Interpretation

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