10.5061/DRYAD.KC20M
Siraj, Amir S.
University of Denver
Bouma, Menno J.
London School of Hygiene & Tropical Medicine
Santos-Vega, Mauricio
University of Chicago
Yeshiwondim, Asnakew K.
University of Denver
Rothman, Dale S.
University of Denver
Yadeta, Damtew
University of Chicago
Sutton, Paul C.
University of Denver
Pascual, Mercedes
University of Chicago
Data from: Temperature and population density determine reservoir regions
of spatial persistence in highland malaria
Dryad
dataset
2015
persistence
reservoir
malaria
2020-06-29T00:00:00Z
en
https://doi.org/10.1098/rspb.2015.1383
163306 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
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
Ce
Central highlands of Ethiopia