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A Process-Based Model with Temperature, Water, and Lab-derived Data Improves Predictions of Daily Mosquito Density
D.P. Shutt, D.W. Goodsman, Z.J.L. Hemez, J.R. Conrad, C. Xu, D. Osthus, C. Russell, J.M. Hyman, C.A. Manore
doi: https://doi.org/10.1101/2021.09.08.458905
D.P. Shutt
fInformation Systems and Modeling, Los Alamos National Laboratory
D.W. Goodsman
aEarth and Environmental Sciences, Los Alamos National Laboratory
bNatural Resources Canada, Northern Forestry Centre
Z.J.L. Hemez
gComputational Physics Division, Los Alamos National Laboratory
J.R. Conrad
cTheoretical Division, Los Alamos National Laboratory
fInformation Systems and Modeling, Los Alamos National Laboratory
C. Xu
aEarth and Environmental Sciences, Los Alamos National Laboratory
D. Osthus
hStatistical Sciences, Los Alamos National Laboratory
C. Russell
dPublic Health Ontario
J.M. Hyman
eDepartment of Mathematics, Tulane University
C.A. Manore
fInformation Systems and Modeling, Los Alamos National Laboratory
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Posted September 09, 2021.
A Process-Based Model with Temperature, Water, and Lab-derived Data Improves Predictions of Daily Mosquito Density
D.P. Shutt, D.W. Goodsman, Z.J.L. Hemez, J.R. Conrad, C. Xu, D. Osthus, C. Russell, J.M. Hyman, C.A. Manore
bioRxiv 2021.09.08.458905; doi: https://doi.org/10.1101/2021.09.08.458905
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