317 research outputs found
The use of census migration data to approximate human movement patterns across temporal scales
No full textHuman movement plays a key role in economies and development, the delivery of services, and the spread of infectious diseases. However, it remains poorly quantified partly because reliable data are often lacking, particularly for low-income countries. The most widely available are migration data from human population censuses, which provide valuable information on relatively long timescale relocations across countries, but do not capture the shorter-scale patterns, trips less than a year, that make up the bulk of human movement. Census-derived migration data may provide valuable proxies for shorter-term movements however, as substantial migration between regions can be indicative of well connected places exhibiting high levels of movement at finer time scales, but this has never been examined in detail. Here, an extensive mobile phone usage data set for Kenya was processed to extract movements between counties in 2009 on weekly, monthly, and annual time scales and compared to data on change in residence from the national census conducted during the same time period. We find that the relative ordering across Kenyan counties for incoming, outgoing and between-county movements shows strong correlations. Moreover, the distributions of trip durations from both sources of data are similar, and a spatial interaction model fit to the data reveals the relationships of different parameters over a range of movement time scales. Significant relationships between census migration data and fine temporal scale movement patterns exist, and results suggest that census data can be used to approximate certain features of movement patterns across multiple temporal scales, extending the utility of census-derived migration data
Seasonal population movements and the surveillance and control of infectious diseases
No full textNational policies designed to control infectious diseases should allocate resources for interventions based on regional estimates of disease burden from surveillance systems. For many infectious diseases, however, there is pronounced seasonal variation in incidence. Policy-makers must routinely manage a public health response to these seasonal fluctuations with limited understanding of their underlying causes. Two complementary and poorly described drivers of seasonal disease incidence are the mobility and aggregation of human populations, which spark outbreaks and sustain transmission, respectively, and may both exhibit distinct seasonal variations. Here we highlight the key challenges that seasonal migration creates when monitoring and controlling infectious diseases. We discuss the potential of new data sources in accounting for seasonal population movements in dynamic risk mapping strategies
Evaluating spatial interaction models for regional mobility in sub-Saharan Africa
No full textSimple spatial interaction models of human mobility based on physical laws have been used extensively in the social, biological, and physical sciences, and in the study of the human dynamics underlying the spread of disease. Recent analyses of commuting patterns and travel behavior in high-income countries have led to the suggestion that these models are highly generalizable, and as a result, gravity and radiation models have become standard tools for describing population mobility dynamics for infectious disease epidemiology. Communities in Sub-Saharan Africa may not conform to these models, however; physical accessibility, availability of transport, and cost of travel between locations may be variable and severely constrained compared to high-income settings, informal labor movements rather than regular commuting patterns are often the norm, and the rise of mega-cities across the continent has important implications for travel between rural and urban areas. Here, we first review how infectious disease frameworks incorporate human mobility on different spatial scales and use anonymous mobile phone data from nearly 15 million individuals to analyze the spatiotemporal dynamics of the Kenyan population. We find that gravity and radiation models fail in systematic ways to capture human mobility measured by mobile phones; both severely overestimate the spatial spread of travel and perform poorly in rural areas, but each exhibits different characteristic patterns of failure with respect to routes and volumes of travel. Thus, infectious disease frameworks that rely on spatial interaction models are likely to misrepresent population dynamics important for the spread of disease in many African populations
Quantifying the impact of accessibility on preventive healthcare in Sub-Saharan Africa using mobile phone data
No full textBackground: Poor physical access to health facilities has been identified as an important contributor to reduced uptake of preventive health services and is likely to be most critical in low-income settings. However, the relation among physical access, travel behavior, and the uptake of healthcare is difficult to quantify. Methods: Using anonymized mobile phone data from 2008 to 2009, we analyze individual and spatially aggregated travel patterns of 14,816,521 subscribers across Kenya and compare these measures to (1) estimated travel times to health facilities and (2) data on the uptake of 2 preventive healthcare interventions in an area of western Kenya: childhood immunizations and antenatal care. Results: We document that long travel times to health facilities are strongly correlated with increased mobility in geographically isolated areas. Furthermore, we found that in areas with equal physical access to healthcare, mobile phone-derived measures of mobility predict which regions are lacking preventive care. Conclusions: Routinely collected mobile phone data provide a simple and low-cost approach to mapping the uptake of preventive healthcare in low-income settings. <br/
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Identifying climate drivers of infectious disease dynamics: recent advances and challenges ahead
No full textClimate change is likely to profoundly modulate the burden of infectious diseases. However, attributing health impacts to a changing climate requires being able to associate changes in infectious disease incidence with the potentially complex influences of climate. This aim is further complicated by nonlinear feedbacks inherent in the dynamics of many infections, driven by the processes of immunity and transmission. Here, we detail the mechanisms by which climate drivers can shape infectious disease incidence, from direct effects on vector life history to indirect effects on human susceptibility, and detail the scope of variation available with which to probe these mechanisms. We review approaches used to evaluate and quantify associations between climate and infectious disease incidence, discuss the array of data available to tackle this question, and detail remaining challenges in understanding the implications of climate change for infectious disease incidence. We point to areas where synthesis between approaches used in climate science and infectious disease biology provide potential for progress.Version of Recor
Quantifying the impact of human mobility on malaria
No full textHuman movements contribute to the transmission of malaria on spatial scales that exceed the limits of mosquito dispersal. Identifying the sources and sinks of imported infections due to human travel and locating high-risk sites of parasite importation could greatly improve malaria control programs. Here, we use spatially explicit mobile phone data and malaria prevalence information from Kenya to identify the dynamics of human carriers that drive parasite importation between regions. Our analysis identifies importation routes that contribute to malaria epidemiology on regional spatial scale
Commentary: Containing the Ebola outbreak – the potential and challenge of mobile network data
No full textThe ongoing Ebola outbreak is taking place in one of the most highly connected and densely populated regions of Africa (Figure 1A). Accurate information on population movements is valuable for monitoring the progression of the outbreak and predicting its future spread, facilitating the prioritization of interventions and designing surveillance and containment strategies. Vital questions include how the affected regions are connected by population flows, which areas are major mobility hubs, what types of movement typologies exist in the region, and how all of these factors are changing as people react to the outbreak and movement restrictions are put in place. Just a decade ago, obtaining detailed and comprehensive data to answer such questions over this huge region would have been impossible. Today, such valuable data exist and are collected in real-time, but largely remain unused for public health purposes - stored on the servers of mobile phone operators. In this commentary, we outline the utility of CDRs for understanding human mobility in the context of the Ebola, and highlight the need to develop protocols for rapid sharing of operator data in response to public health emergencies
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Infectious Disease Modeling: Enhancing Epidemic Preparedness and Response
No full textRecent outbreaks of Ebola, Zika, and COVID-19, among others, have shown how infectious diseases can decimate economies and destroy lives. Infectious disease models are important tools for preparing for, preventing, and responding to such epidemics. Here, we use infectious disease modeling to analyze past outbreaks, prepare for future outbreaks, and respond to ongoing outbreaks, with the goal of informing public health response.
We first analyze past Ebola and cholera outbreaks and build a simulation model to understand the role the incubation period, the time between exposure and symptom onset, has on epidemic trajectory. We find that diseases with longer incubation periods, such as Ebola, where infected individuals can travel further before becoming infectious, result in more long-distance sparking events and less predictable disease trajectories, as compared to the more predictable wave-like spread of diseases with shorter incubation periods, such as cholera. Second, we assess if augmenting classical randomized controlled trials of vaccines with pathogen sequence and contact tracing data can permit these trials to estimate vaccine efficacy against infectiousness, or the reduction in onward transmission from a vaccinated person who is infected compared to an unvaccinated infected person. Through simulations of a transmission model and a vaccine trial, we find that these data sources enhance identifiability of this key measure of vaccine efficacy. Finally, we simulate studies of SARS-CoV-2 seroprotection. We find that in studies assessing whether seropositivity confers protection against future infection, time varying epidemic dynamics can cause confounding; it is therefore necessary to adjust for geographic location and time of enrollment in order to reduce bias. These methods and findings demonstrate how infectious disease modeling can be used to enhance epidemic preparedness and response
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