Distance sampling for epidemiology: an interactive tool for estimating under-reporting of cases from clinic data

Abstract

Background: Distance sampling methods are widely used in ecology to estimate and map the abundance of animal and plant populations from spatial survey data. The key underlying concept in distance sampling is the detection function, the probability of detecting the occurrence of an event as a function of its distance from the observer, as well as other covariates that may influence detection. In epidemiology, the burden and distribution of infectious disease is often inferred from cases that are reported at clinics and hospitals. In areas with few public health facilities and low accessibility, the probability of detecting a case is also a function of the distance between an infected person and the "observer" (e.g. a health centre). While the problem of distance-related under-reporting is acknowledged in public health; there are few quantitative methods for assessing and correcting for this bias when mapping disease incidence. Here, we develop a modified version of distance sampling for prediction of infectious disease incidence by relaxing some of the framework's fundamental assumptions. We illustrate the utility of this approach using as our example malaria distribution in rural Burkina Faso, where there is a large population at risk but relatively low accessibility of health facilities. Results: The modified distance-sampling framework was used to predict the probability of reporting malaria infection at 8 rural clinics, based on road-travel distances from villages. The rate at which reporting probability dropped with distance varied between clinics, depending on road and clinic positions. The probability of case detection was estimated as 0.3-1 in the immediate vicinity of the clinic, dropping to 0.1-0.6 at a travel distance of 10 km, and effectively zero at distances > 30-40 km. Conclusions: To enhance the method's strategic impact, we provide an interactive mapping tool (as a self-contained R Shiny app) that can be used by non-specialists to interrogate model outputs and visualize how the overall probability of under-reporting and the catchment area of each clinic is influenced by changing the number and spatial allocation of health centres.

Surveillance Routing of COVID-19 Infection Spread Using an Intelligent Infectious Diseases Algorithm

“…Some similar proposals found in the literature are the research of Nelli (2020) [12] and Kang (2020) [15]. In Nelli (2020), a model is presented to predict the probability of Malaria infection in eight rural clinics, based on the road travel distances from the surrounding villages (Burkina Faso).…”

“…A list of hospitals in New York state with Pulmonology and Lung surgery departments was obtained from the New [6] 2016 HIV genotype ----Sung [17] 2016 EMS data -----Gryseels [7] 2017 GenBank ----Wesolowski [8] 2017 GISDiva --Guzzetta [9] 2018 Dengue Transmission ---Ray [13] 2018 Influenza ----Li [10] 2019 A(H7N9) ---Nandana [14] 2019 Delhi-Dengue ---Grabowski [11] 2020 RCCS -----Nelli [12] 2020 Malaria ---Kang [15] 2020 Covid-19 China ---d'Onofrio [16] 2020 ----Guevara 2020 Covid-19 NY, USA -- York Department of Health (https://profiles.health.ny.gov).…”

Sustained Malaria Transmission despite Reactive Screen-and-Treat in a Low-Transmission Area of Southern Zambia

“… 9 , 10 , 12 , 21 , 22 , 38 Many previous studies have focused on the spatial extent and methods for screening and timing of potential follow-up visits in programs. 21 , 23 , 39 , 40 Reactive screen-and-treat is now being implemented across several sub-Saharan African countries using similar methods as in southern Zambia. 9 , 11 , 16 These have led to improved surveillance and an identification of an increased number of parasitemic individuals but have yet to successfully reduce transmission or achieve elimination.…”

Constructing and validating a transferable epidemic risk index in data scarce environments using open data: a case study for dengue in the Philippines

“…Known factors related to underreporting, other than possible asymptomatic cases [22], are sociodemographic factors that impede health-seeking behavior, such as poverty and education [23, 24], and geographical accessibility to health facilities [25–27]. Understanding and quantifying these factors is thus necessary to assess disease incidence, and thus epidemic risk, at a local level [26]. While the socioeconomic indicators which affect self-reporting are usually difficult to measure at a population-level and their relative importance is highly dependent on the local context [23], data on the location of health facilities is available virtually world-wide (but with various degrees of completeness) and their geographical accessibility can be modeled with suitable geo-spatial tools [25, 28].…”

“…Targeting regions for vector control measures is of high importance to optimize and maximize the effect of the available resources [29, 30]. Understanding the distribution of dengue risk is key in tailoring and targeting intervention strategies on sub-national scales [30], but challenging due to underreporting [26]. Improved methods are needed to meet the recently updated WHO NTD roadmap target of a 0% dengue case facitliy rate (CFR) by 2030 (from the 0.8% baseling in 2020) [32].…”