TY - JOUR
T1 - Assessing the spatiotemporal malaria transmission intensity with heterogeneous risk factors
T2 - A modeling study in Cambodia
AU - Liu, Mutong
AU - Liu, Yang
AU - Po, Ly
AU - Xia, Shang
AU - Huy, Rekol
AU - Zhou, Xiao Nong
AU - Liu, Jiming
N1 - Funding Information:
This research was funded by the Ministry of Science and Technology of China (2021ZD0112501/2021ZD0112502), the HKSAR Research Grants Council (12201318/12201619/12202220), and the HKBU/CSD Departmental Start-up Fund for New Assistant Professors.
Publisher Copyright:
© 2023 The Authors.
PY - 2023/3
Y1 - 2023/3
N2 - Malaria control can significantly benefit from a holistic and precise way of quantitatively measuring the transmission intensity, which needs to incorporate spatiotemporally varying risk factors. In this study, we conduct a systematic investigation to characterize malaria transmission intensity by taking a spatiotemporal network perspective, where nodes capture the local transmission intensities resulting from dominant vector species, the population density, and land cover, and edges describe the cross-region human mobility patterns. The inferred network enables us to accurately assess the transmission intensity over time and space from available empirical observations. Our study focuses on malaria-severe districts in Cambodia. The malaria transmission intensities determined using our transmission network reveal both qualitatively and quantitatively their seasonal and geographical characteristics: the risks increase in the rainy season and decrease in the dry season; remote and sparsely populated areas generally show higher transmission intensities than other areas. Our findings suggest that: the human mobility (e.g., in planting/harvest seasons), environment (e.g., temperature), and contact risk (coexistences of human and vector occurrence) contribute to malaria transmission in spatiotemporally varying degrees; quantitative relationships between these influential factors and the resulting malaria transmission risk can inform evidence-based tailor-made responses at the right locations and times.
AB - Malaria control can significantly benefit from a holistic and precise way of quantitatively measuring the transmission intensity, which needs to incorporate spatiotemporally varying risk factors. In this study, we conduct a systematic investigation to characterize malaria transmission intensity by taking a spatiotemporal network perspective, where nodes capture the local transmission intensities resulting from dominant vector species, the population density, and land cover, and edges describe the cross-region human mobility patterns. The inferred network enables us to accurately assess the transmission intensity over time and space from available empirical observations. Our study focuses on malaria-severe districts in Cambodia. The malaria transmission intensities determined using our transmission network reveal both qualitatively and quantitatively their seasonal and geographical characteristics: the risks increase in the rainy season and decrease in the dry season; remote and sparsely populated areas generally show higher transmission intensities than other areas. Our findings suggest that: the human mobility (e.g., in planting/harvest seasons), environment (e.g., temperature), and contact risk (coexistences of human and vector occurrence) contribute to malaria transmission in spatiotemporally varying degrees; quantitative relationships between these influential factors and the resulting malaria transmission risk can inform evidence-based tailor-made responses at the right locations and times.
KW - Computational approach
KW - Heterogeneous risk factors
KW - Malaria
KW - Spatiotemporal network
KW - Transmission intensity assessment
UR - http://www.scopus.com/inward/record.url?scp=85148339906&partnerID=8YFLogxK
U2 - 10.1016/j.idm.2023.01.006
DO - 10.1016/j.idm.2023.01.006
M3 - Journal article
AN - SCOPUS:85148339906
SN - 2468-0427
VL - 8
SP - 253
EP - 269
JO - Infectious Disease Modelling
JF - Infectious Disease Modelling
IS - 1
ER -