DOI: 10.5061/dryad.kc20m
title: Data from: Temperature and population density determine reservoir regions of spatial persistence in highland malaria

description: A better understanding of malaria persistence in highly seasonal
 environments such as highlands and desert fringes requires identifying the
 factors behind the spatial reservoir of the pathogen in the low season. In
 these ‘unstable’ malaria regions, such reservoirs play a critical role by
 allowing persistence during the low transmission season and therefore,
 between seasonal outbreaks. In the highlands of East Africa, the most
 populated epidemic regions in Africa, temperature is expected to be
 intimately connected to where in space the disease is able to persist
 because of pronounced altitudinal gradients. Here, we explore other
 environmental and demographic factors that may contribute to
 malaria's highland reservoir. We use an extensive spatio-temporal
 dataset of confirmed monthly Plasmodium falciparum cases from 1995 to 2005
 that finely resolves space in an Ethiopian highland. With a Bayesian
 approach for parameter estimation and a generalized linear mixed model
 that includes a spatially structured random effect, we demonstrate that
 population density is important to disease persistence during the low
 transmission season. This population effect is not accounted for in
 typical models for the transmission dynamics of the disease, but is
 consistent in part with a more complex functional form of the force of
 infection proposed by theory for vector-borne infections, only during the
 low season as we discuss. As malaria risk usually decreases in more urban
 environments with increased human densities, the opposite counterintuitive
 finding identifies novel control targets during the low transmission
 season in African highlands.
description: JFMA cases and the
 covariates tested and used in our modelThis data has all
 variables used in the statistical model as they entered the generalized
 linear model and the generalized linear mixed model. The variables
 included are (in the order they appear): year, kebeleID, JFMA total cases,
 log expected cases, scaled log ratio of SOND cases to the expected SOND
 cases, scaled DJF mean temperature in degree Celsius, scaled DJF total
 rainfall in mm, scaled population density from overlapping circles of 5km
 radius, scaled population density from overlapping circles of 10km radius,
 scaled weighted distance to roads, scaled inverse square distance to
 perennial water bodies, scaled average soil water holding capacity, scaled
 average slope, scaled average NDVI, scaled SST anomalies from the Nino 3.4
 region, and IRS status (0/1).covariates_std.csvCount of neighboring kebelesThis data set contains the count of kebeles neighboring each kebele. This file should be used in combination with the Nieghborhood.csv. For example the first kebele (ID=1) has 4 neighbors. Thus, the first four numbers in neighborhood.csv are kebele ID's of those kebeles neighboring kebele 1. Similarly, the second kebele has 3 neighbors, and thus the next three number in neighborhood.csv are IDs of its three neighbors. The remaining neighbors are identified by matching them with corresponding kebele IDs in the file neighborhood.csv in this manner.num_neighbors.csvneighborhoodThis data set contains the IDs of kebeles neighboring each kebele in the file named "Count of neighboring kebeles". This file should be used in combination with the "Count of neighboring kebeles". For example the first kebele (ID=1) has 4 neighbors. The first four number in this data set are the kebele ID's of those kebeles neighboring kebele 1. Similarly, the second kebele has 3 neighbors, and the next three number in this data set are IDs of its three neighbors. The remaining neighbors are identified by matching them with corresponding kebele IDs in similar manner.neighbor_geogID.csv