Assessing the potential impact of vector-borne disease transmission following heavy rainfall events: a mathematical framework

Abstract

Predicting the impact of natural disasters such as hurricanes on the transmission dynamics of infectious diseases poses significant challenges. In this paper, we put forward a simple modelling framework to investigate the impact of heavy rainfall events (HREs) on mosquito-borne disease transmission in temperate areas of the world such as the southern coastal areas of the USA. In particular, we explore the impact of the timing of HREs relative to the transmission season via analyses that test the sensitivity of HRE-induced epidemics to variation in the effects of rainfall on the dynamics of mosquito breeding capacity, and the intensity and temporal profile of human population displacement patterns. The recent Hurricane Harvey in Texas motivates the simulations reported. Overall, we find that the impact of vector-borne disease transmission is likely to be greater the earlier the HREs occur in the transmission season. Simulations based on data for Hurricane Harvey suggest that the limited impact it had on vector-borne disease transmission was in part because of when it occurred (late August) relative to the local transmission season, and in part because of the mitigating effect of the displacement of people. We also highlight key data gaps related to models of vector-borne disease transmission in the context of natural disasters. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.

Keywords: climate change; heavy rainfall event; mathematical model; mosquito-borne disease; transmission dynamics; vector-borne disease.

Figure 1.

(a) Daily temperature and rainfall curves employed for assessing the impact of the timing of the HRE on the epidemic attack rate. We assumed four different 4-day North Atlantic hurricanes characterized by sustained rainfall at 50 cm per day occurring on 1 June, 1 July, 1 August or 1 September and a baseline (nonhurricane) rainfall per day at 0.5 cm, together with a temperature cycle that is consistent with that of the evacuation counties in Texas during Hurricane Harvey. (b) Daily temperature and rainfall time series for each of the mandatory evacuation counties in Texas (Arkansas, Brazoria, Calhoun, Jackson, Matagorda, Refugio, San Patricio and Victoria) used for Hurricane Harvey simulation scenarios.

Figure 2.

Three response curves (low, moderate, high) for $H_i(t)$ describing the proportion of the displaced population in a given area i relative to the timing and duration of the HRE that were modelled using two logistic functions: (i) the proportion of the displaced population rapidly starts to increase on 21 August 2017 until a maximum displacement level $H_{max}$ is reached 1 day later and (ii) the proportion displaced gradually declines from $H_{max}$ until baseline pre-disaster levels return 100 days later. The values of $H_{max}$ were informed by our tweet-based analyses suggesting that the proportion of the displaced population during Hurricane Harvey ranged from 38 to 80%.

Figure 3.

The relation between the timing of an HRE event and the proportion of the population displaced on the cumulative number of cases for the four different hypothetical 4-day hurricane scenarios characterized by sustained rainfall at 50 cm per day occurring on 1 June, 1 July, 1 August or 1 September and a baseline (non-hurricane) rainfall per day at 0.5 cm together with a temperature cycle that is consistent with that of the evacuation counties in Texas during Hurricane Harvey.

Figure 4.

Four different time snapshots of tweeting activity in the mandatory evacuation counties in Texas before (a), during (b,c) and after (d) Hurricane Harvey.

Figure 5.

Daily series of the number of tweets before, during and after Hurricane Harvey generated in the mandatory evacuation counties in Texas.