In an increasingly crowded and connected world, infectious diseases can spread rapidly between regions, as highlighted by increasingly frequent global pandemics including SARS, H1N1 influenza, Ebola virus, and now Zika virus. The spatial spread of disease mediated by human mobility also impacts endemic pathogens like malaria, where control programs and elimination strategies are undermined by travel to and from high transmission regions, drug resistant parasites are spread by human mobility, and distinguishing local from imported cases is critical for planning interventions. Understanding the distribution and dynamics of human populations underlies all aspects of infectious disease control, from the interpretation of surveillance data to the allocation of resources. Until recently, however, there was a glaring lack of information about human mobility patterns that spread diseases, particularly in low-income settings. New sources of data on human mobility and the spatial spread of diseases are increasingly available. In particular, data from mobile phones provide passively collected, real-time information on the scale of millions of individuals, with operators routinely collecting data on the cell towers associated with calls/texts that ? when appropriately anonymized ? can be modeled to provide longitudinal maps of where people are and how they are moving. We have been developing approaches to these models into epidemiological frameworks for understanding the spatial spread of infections, showing that these approaches provide specific targets for malaria control, accurate predictions about the location and timing of dengue epidemics, and insights into seasonal peaks of rubella, for example. Sequencing technology is also producing large volumes of geocoded pathogen genomic data, which can be used to estimate gene flow between populations ? a measure of the rate at which infections are spreading. We have been analyzing malaria genetic data to adapt standard population genetic methods to accommodate the complex lifecycle and high diversity of the malaria parasite, in order to estimate this internal measure of migration. This proposal brings together these sources of information about the spatial spread of infectious diseases, focusing on the spread of the malaria parasite in Southeast Asia, working with collaborators collecting parasite genomic data in the region, mobile operators, and National Malaria Control Programs, to develop practical mathematical tools for integrating mobility data and pathogen genomics into the risk mapping, drug resistance monitoring, and resource allocation protocols used by control programs when planning for elimination. The project will lead to an analytical pipeline for generating mobility models from mobile phone data that can also be applied to other infectious diseases, and in particular in response to emerging epidemics. New tools are needed to understand the interaction between human population dynamics and the spread of infectious disease threats. These data sets are now increasingly straightforward to generate, but the analytical tools available to make the most use of them are still lacking. This proposal aims to develop the approaches to translate the promise of ?Big Data? into insights that can be used by policy makers to control and contain human pathogens.
Understanding how human mobility patterns spread infectious diseases is key to controlling and containing epidemics, and to the elimination of endemic pathogens such as malaria. Here, we will use new sources of data from mobile phones and pathogen genomics to estimate the spatial dynamics of infections, focusing on the spread of malaria in Southeast Asia. By developing new methods that use these complementary data types, we will provide more accurate risk maps and tools that can be used to allocate resources efficiently, as well as to contain epidemics when they emerge.
| Wesolowski, Amy; Taylor, Aimee R; Chang, Hsiao-Han et al. (2018) Mapping malaria by combining parasite genomic and epidemiologic data. BMC Med 16:190 |
| Taylor, Aimee R; Schaffner, Stephen F; Cerqueira, Gustavo C et al. (2017) Quantifying connectivity between local Plasmodium falciparum malaria parasite populations using identity by descent. PLoS Genet 13:e1007065 |
