Discovering environmental management opportunities for infectious disease control

Abstract

Climate change and emerging drug resistance make the control of many infectious diseases increasingly challenging and diminish the exclusive reliance on drug treatment as sole solution to the problem. As disease transmission often depends on environmental conditions that can be modified, such modifications may become crucial to risk reduction if we can assess their potential benefit at policy-relevant scales. However, so far, the value of environmental management for this purpose has received little attention. Here, using the parasitic disease of fasciolosis in livestock in the UK as a case study, we demonstrate how mechanistic hydro-epidemiological modelling can be applied to understand disease risk drivers and the efficacy of environmental management across a large heterogeneous domain. Our results show how weather and other environmental characteristics interact to define disease transmission potential and reveal that environmental interventions such as risk avoidance management strategies can provide a valuable alternative or complement to current treatment-based control practice.

Figure 1

Seasonal abundance of metacercariae, i.e. disease risk simulated using HELF, on average over 2007–2015, across 9 UK regions. Boxplots represent variability in disease risk between catchments within regions. In the map (created using Matlab R2019a), ungauged catchments -i.e. with no hydrological data over the simulation period- are masked in grey. SW South West of England and West Wales, Mid rest of Wales and Midlands, NE North East of England, NScot North of Scotland, WScot West of Scotland, SE South East of England, EAng East Anglia, EScot East of Scotland, NW North West of England.

Figure 2

Average percent contribution of the top three environmental drivers to summer disease risk variability simulated using HELF, per region. Rainfall-related characteristics are in blue (RD = number of rainy days and R = rainfall); temperature-related variables are in red (P = potential evapotranspiration and T = temperature); landscape factors are in green (TOPO = topography); and two-way interactions between factors (here combined into a single term INT) are in yellow. Regions are defined in Fig. 1.

Figure 3

Effect of treating livestock twice per year (in January and April) using the current most efficient antiparasitic drug (90%) and assuming no resistance, on: (a) summer disease risk across all 935 analyzed catchments; (b) the monthly abundance of infective metacercariae on pasture (i.e. disease risk), on average across all catchments and years.

Figure 4

Effectiveness of environmental interventions (maps created using Matlab R2019a): (a) Percentage of catchment area we would have to exclude from livestock grazing to reduce summer risk of infection by at least the same percentage achieved using treatment (on average across catchments within each region and for year 2013 as an example within our simulation period—but also see Figure S2 for a comparison with a wetter year); (b) Percentage increase in drainage that would be needed, compared to current conditions, to match as close as possible the reduction in summer risk of infection achieved using treatment (on average across catchments within each region).