The Zero-Corrected, Gravity-Model Multiplier (ZERO-G): A novel method to estimate disease dynamics at the community-scale from passive surveillance data

Data on population health are vital to evidence-based decision making by public health officials, but are rarely adequately localized, particularly in rural areas where barriers to healthcare can result in extremely low ascertainment of cases by the health system. Here, we demonstrate a new method to estimate disease incidence at the community level from passive surveillance data collected at primary health centers. The zero-corrected, gravity-based multiplier (ZERO-G) method explicitly models sampling intensity as a function of health facility characteristics and statistically accounts for extremely low rates of ascertainment, resulting in an unbiased, standardized estimate of disease incidence at a spatial resolution nearly ten times finer than typically reported by the facility-based passive surveillance system. We assessed the robustness of this method by applying it to a case study of malaria incidence in a rural health district in southeastern Madagascar. ZERO-G decreased geographic and financial bias in the dataset by over 64% and doubled the agreement rate between spatial patterns in malaria incidence and prevalence rates. ZERO-G can be applied to other infectious diseases and settings, increasing the availability of long-term, high quality surveillance datasets at the community scale.