Efficient Mitigation Strategies for Epidemics in Rural Regions

Abstract

Containing an epidemic at its origin is the most desirable mitigation. Epidemics have often originated in rural areas, with rural communities among the first affected. Disease dynamics in rural regions have received limited attention, and results of general studies cannot be directly applied since population densities and human mobility factors are very different in rural regions from those in cities. We create a network model of a rural community in Kansas, USA, by collecting data on the contact patterns and computing rates of contact among a sampled population. We model the impact of different mitigation strategies detecting closely connected groups of people and frequently visited locations. Within those groups and locations, we compare the effectiveness of random and targeted vaccinations using a Susceptible-Exposed-Infected-Recovered compartmental model on the contact network. Our simulations show that the targeted vaccinations of only 10% of the sampled population reduced the size of the epidemic by 34.5%. Additionally, if 10% of the population visiting one of the most popular locations is randomly vaccinated, the epidemic size is reduced by 19%. Our results suggest a new implementation of a highly effective strategy for targeted vaccinations through the use of popular locations in rural communities.

Viral conductance: Quantifying the robustness of networks with respect to spread of epidemics

“…For more details about the survey questions and the link weights, we refer the reader to [31]. We applied the measure VC to quantify the robustness of the social network with respect to the spread of SIS epidemics.…”

Information networks for disease: commonalities in human management networks and within-host signalling networks

“…A primary behaviour change in response to information about human epidemics may be change in how frequently people choose to encounter others and other attempts to avoid exposure, which may or may not slow epidemics (Del Valle et al 2005;Meloni et al 2011). Increasingly information is available about patterns of interaction among study groups of people (Edmunds et al 1997;Eubank et al 2004;Mossong et al 2008;Scoglio et al 2010). Individuals as nodes may choose to modify their network links, such that they have less exposure to potential infection sources (Gross et al 2006).…”

Identifying multiple infection sources in a network

“…�EI' i=l ) _ I T p(Ui,Uj) (51, 52)1 q Ut,UJ,51,52 -( ( ) ( )) q Ui+1, Uji 51, 52 + qU i, Uj -1i 51, 52 (9) for i < j, and q (v , Vi 51, 52) = ITv(51, 52)1 -1…”

“…The source identification problem also arises in the study and control of viral epidemics. The identification of index cases of a contagious disease in a human population allows us to study the causes, and hence facilitate the search for antiviral drugs and efficacious therapies [9], [10].…”