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

Abstract

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.

Figure 1

Dengue incidence and government interventions in Latin America and southeast Asia in 2020 versus 2014–19 (A) Relative change ratio of annual dengue incidence in 2020 versus the mean incidence in 2014–19. (B) Distribution of relative change ratio of annual dengue incidence for each country in 2020 versus 2019. The boxplot displays 2·5th, 25th, 50th,75th and 97·5th percentiles. (C) The relative change ratio of monthly dengue incidence in 2020 relative to the monthly mean incidence in 2014–19. (D) Change of government stringency index against COVID-19 in 2020. The black line represents the beginning of a consistent dengue incidence decline in 2020 versus the monthly mean in 2014–19.

Figure 2

Strength of association between dengue risk and public health and social measures and human mobility behaviours (A) Dendrogram showing the hierarchical clustering of public health and social measures and human mobility behaviours timeseries. The height of nodes connecting two variables on the dendrogram represents the degree of similarity. For example, the school closing variable is more similar to the cancel public events variable than it is to the restrictions on gathering size variable. (B) Data show the strength of evidence of association between dengue risk and either public health and social measures or human movement behaviours. Variables are coloured according to their respective clusters. All columns except the first refer to the multivariable model. For terms with p<0.05, the direction of RR is given. RRs were calculated cumulatively over all lag periods and compare the variables at their strictest (1 and 100%) with baseline pre-pandemic levels, with 95% CIs. *Clusters with approximately unbiased p values larger than 95% are classified as significant clusters. NS=not significant (p>0·05). RR=relative risk.

Figure 3

Association between different selected intervention and human movement variables with dengue risk over different lags The index for public health and social measures ranges from 0 to 100. A higher score indicates a more stringent, more geographically comprehensive COVID-19 response policy (0 for no response policy and 100 for the most stringent response policy). The baseline of human mobility was the median for the first 5 weeks of 2020 (Jan 3–Feb 6), which was defined as 100%. (A) Contour plot of the association between selected intervention and human movement variables and risk of dengue, relative to the baseline, without government interventions (ie, 0 for public health and social measures and 100 for human movement behaviours). The deeper the shade of red, the greater the increase in relative risk of dengue compared with the baseline. The deeper the shade of blue, the greater the decrease in relative risk of dengue compared with the baseline. (B) Dengue lag–response association for loose, moderate, and strict government interventions relative to the baseline. (C) Cumulative lags over the 4-month time periods associations between public health and social measures or human movement behaviours and risk of dengue, relative to the baseline, without government interventions. Shaded regions are 95% CIs. Predictions are from the intervention models. Cumulative lags over the 4-month time periods are shown in the appendix (p 10).

Figure 4

Preventive fraction of dengue cases averted in 2020 attributable to specific public health and social measures and human movement behaviours, by region and country Preventive fractions were calculated using the intervention models.