Modelling Environmentally-Mediated Infectious Diseases of Humans: Transmission Dynamics of Schistosomiasis in China

Abstract

M acroparasites of humans are sensitive to a variety of environmental variables, including temperature, rainfall and hydrology, yet current comprehension of these relationships is limited. Given the incomplete mechanistic understanding of environment-disease interactions, mathematical models that describe them have seldom included the effects of time-varying environmental processes on transmission dynamics and where they have been included, simple generic, periodic functions are usually used. Few examples exist where seasonal forcing functions describe the actual processes underlying the environmental drivers of disease dynamics. Transmission of human schistosomes, which involves multiple environmental stages, offers a model for applying our understanding of the environmental determinants of the viability, longevity, infectivity and mobility of these stages to controlling disease in diverse environments.Here, a mathematical model of schistosomiasis transmission is presented which incorporates the effects of environmental variables on transmission. Model dynamics are explored and several key extensions to the model are proposed.Modelling Parasite Transmission and Control, edited by Edwin Michael and Robert C. Spear.

The spatial spread of schistosomiasis: A multidimensional network model applied to Saint-Louis region, Senegal

“…Early studies on the heterogeneity of schistosomiasis transmission (Barbour, 1978) already introduced a partitioning between human and animal host populations, but did not consider physical connectivity through the environment, an approach followed also in later works (Gurarie, King, 2005, Gurarie, King, Wang, 2010, Woolhouse, Etard, Dietz, Ndhlovu, Chandiwana, 1998, Woolhouse, Watts, Chandiwana, 1991). On the other hand, other studies (Gurarie and Seto, 2009; Perez-Saez et al., 2015; Xu et al., 2006) did consider the role of environmental connectivity (typically through larval dispersal alone), while at the same time neglecting the possible spatial mismatch between villages and water contact points (but see Remais, 2010, in which, however, snail dispersal is neglected). In our work, instead, n v villages and n w water points constitute two distinct sets of nodes of a fully coupled, multi-layered (multidimensional, sensu Boccaletti et al., 2014), spatially explicit network.…”

“…On the human host side, social connections provide a pathway for adult parasite transport while people travel between endemic and non-endemic areas. This movement can involve very large spatial scales in ways that are often difficult to predict (Remais, 2010), and constitutes an effective transmission mechanism provided that disease-transmitting snails live in the visited areas. On the snail and parasite side, connectivity via physical processes (hydrological transport and animal dispersal) increases the risk of larval and snail propagation over shorter spatial scales.…”

Schistosomiasis japonica: Modelling as a tool to explore transmission patterns

“…First, the role of risk factors impacting on schistosomiasis transmission varies with the spatial scale, e.g. individual factors including individual behaviour, health habits, individual immunity are all important at for small-scale changes of the transmission process, while ecological factors including distance to the positive snail sites, vegetation of snail sites are large-scale variables Remais, 2010;Zhou et al, 2008). Second, the development of the schistosome stage inside the snail is mainly affected by temperature, which has been used for the study of the impact of climate changes on the distribution of schistosomiasis japonica.…”

“…These statistical models are quantitative, biomedical tools, commonly used to summarise data related to epidemiological studies (Guyatt, 1998). Mathematical dynamic models applied for communicable diseases, on the other hand, emphasise the factors responsible for the mechanisms of transmission, dynamics of the infectious processes, for example providing threshold conception, such as the basic reproductive number (R 0 ), estimation of key parameters reflecting the various factors associated with transmission, elaborating transmission mechanism, and optimising control strategies (Chiyaka et al, 2010;Hu et al, 2010;Remais, 2010).…”

Model approaches for estimating the influence of time-varying socio-environmental factors on macroparasite transmission in two endemic regions

“…Mathematical models are useful for exploring these environment-disease interactions (Remais 2008), yet seasonality is commonly implemented phenomenologically, using simple mathematical functions that are periodic in time and therefore describe in a generic way the seasonal variation in a parameter – a sinusoidal function is common. Few examples exist where seasonal factors describe the actual processes underlying the intensity of disease transmission (Kendall, Briggs et al 1999).…”