Spatiotemporal analysis of surveillance data enables climate-based forecasting of Lassa fever

Abstract

Lassa fever (LF) is an acute rodent-borne viral haemorrhagic fever that is a longstanding public health concern in West Africa and increasingly a global health priority. Recent molecular studies1,2 have confirmed the fundamental role of the rodent host (Mastomys natalensis) in driving human infections, but LF control and prevention efforts remain hampered by a limited baseline understanding of the disease’s true incidence, geographical distribution and underlying drivers3. Here, through analysing 8 years of weekly case reports (2012-2019) from 774 local government authorities (LGAs) across Nigeria, we identify the socioecological correlates of LF incidence that together drive predictable, seasonal surges in cases. At the LGA-level, the spatial endemic area of LF is dictated by a combination of rainfall, poverty, agriculture, urbanisation and housing effects, although LF’s patchy distribution is also strongly impacted by reporting effort, suggesting that many infections are still going undetected. We show that spatial patterns of LF incidence within the endemic area, are principally dictated by housing quality, with poor-quality housing areas seeing more cases than expected. When examining the seasonal and inter-annual variation in incidence within known LF hotspots, climate dynamics and reporting effort together explain observed trends effectively (with 98% of observations falling within the 95% predictive interval), including the sharp uptick in 2018-19. Our models show the potential for forecasting LF incidence surges 1-2 months in advance, and provide a framework for developing an early-warning system for public health planning.