This study presents a case study of how social network and spatial analytical methods can be used simultaneously for disease transmission modeling. The paper first reviews strategies employed in previous studies and then offers the example of transmission of two bacterial diarrheal diseases in rural Bangladesh. The goal is to understand how diseases vary socially above and beyond the effects of the local neighborhood context. Patterns of cholera and shigellosis incidence are analyzed in space and within kinship-based social networks in Matlab, Bangladesh. Data include a spatially referenced longitudinal demographic database which consists of approximately 200,000 people and laboratory-confirmed cholera and shigellosis cases from 1983 to 2003. Matrices are created of kinship ties between households using a complete network design and distance matrices are also created to model spatial relationships. Moran's I statistics are calculated to measure clustering within both social and spatial matrices. Combined spatial effects-spatial disturbance models are built to simultaneously analyze spatial and social effects while controlling for local environmental context. Results indicate that cholera and shigellosis always clusters in space and only sometimes within social networks. This suggests that the local environment is most important for understanding transmission of both diseases however kinship-based social networks also influence their transmission. Simultaneous spatial and social network analysis can help us better understand disease transmission and this study has offered several strategies on how.

Infectious diseases often cluster spatially, but can also cluster socially because they are transmitted within social networks. This study compares spatial and social clustering of cholera in rural Bangladesh. Data include a spatially referenced longitudinal demographic database which consists of approximately 200,000 people and laboratory-confirmed cholera cases from 1983 to 2003. Matrices are created of kinship ties between households using a complete network design and distance matrices are also created to model spatial relationships. Moran's I statistics are calculated to measure clustering within both social and spatial matrices. The results show that cholera always clusters in space and seldom within social networks. Cholera is transmitted mostly through the local environment rather than through person-to-person contact. Comparing spatial and social network analysis can help improve understanding of disease transmission.

BackgroundThis study investigates the impact of tubewell user density on cholera and shigellosis events in Matlab, Bangladesh between 2002 and 2004. Household-level demographic, health, and water infrastructure data were incorporated into a local geographic information systems (GIS) database. Geographically-weighted regression (GWR) models were constructed to identify spatial variation of relationships across the study area. Zero-inflated negative binomial regression models were run to simultaneously measure the likelihood of increased magnitude of disease events and the likelihood of zero cholera or shigellosis events. The aim of this study was to examine the effect of tubewell density on both the occurrence of diarrheal disease and the magnitude of diarrheal disease incidence.ResultsIn Matlab, households with greater tubewell density were more likely to report zero cholera or shigellosis events. Results for both cholera and shigellosis GWR models suggest that tubewell density effects are spatially stationary and the use of non-spatial statistical methods is appropriate.ConclusionsIncreasing the amount of drinking water available to households through increased density of tubewells contributed to lower reports of cholera and shigellosis events in rural Bangladesh. Our findings demonstrate the importance of tubewell installation and access to groundwater in reducing diarrheal disease events in the developing world.

BackgroundTraditional vaccine trial methods have an underlying assumption that the effect of a vaccine is the same throughout the trial area. There are, however, many spatial and behavioral factors that alter the rates of contact among infectious and susceptible individuals and result in different efficacies across a population. We reanalyzed data from a field trial in Bangladesh to ascertain whether there is evidence of indirect protection from cholera vaccines when vaccination rates are high in an individual's social network.MethodsWe analyzed the first year of surveillance data from a placebo-controlled trial of B subunit-killed whole-cell and killed whole-cell-only oral cholera vaccines in children and adult women in Bangladesh. We calculated whether there was an inverse trend for the relation between the level of vaccine coverage in an individual's social network and the incidence of cholera in individual vaccine recipients or placebo recipients after controlling for potential confounding variables.ResultsUsing bari-level social network ties, we found incidence rates of cholera among placebo recipients were inversely related to levels of vaccine coverage (5.28 cases per 1000 in the lowest quintile vs 3.27 cases per 1000 in the highest quintile; p = 0.037 for trend). Receipt of vaccine by an individual and the level of vaccine coverage of the individual's social network were independently related to a reduced risk of cholera.ConclusionsFindings indicate that progressively higher levels of vaccine coverage in bari-level social networks can lead to increasing levels of indirect protection of non-vaccinated individuals and could also lead to progressively higher levels of total protection of vaccine recipients.

We develop novel methods for conceptualizing geographic space and social networks to evaluate their respective and combined contributions to childhood diarrheal incidence. After defining maternal networks according to direct familial linkages between females, and road networks using satellite imagery of the study area, we use a spatial econometrics model to evaluate the significance of correlation terms relating childhood diarrheal incidence to the incidence observed within respective networks. Disease was significantly clustered within road networks across time, but only inconsistently correlated within maternal networks. These methods could be widely applied to systems in which both social and spatial processes jointly influence health outcomes.