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The predictive power of the monthly model and test sets (10..:

Weerapong Thanapongtharm (118229) ; Sarin Suwanpakdee (119683) ; Arun Chumkaeo (11778329)..
https://figshare.com/articles/figure/The_predictive_power_of_the_monthly_model_and_test_sets_100_bootstraps_/17107727. , 2021

Link: https://doi.org/10.1371/journal.pntd.0009980.g004

RT Journal
T1 <Title>The predictive power of the monthly model and test sets (100 bootstraps)</Title>

UL https://suche.suub.uni-bremen.de/peid=base-ftsmithonian:oai:figshare.com:article_17107727&Exemplar=1&LAN=DE
				
A1 Weerapong Thanapongtharm (118229)
A1 Sarin Suwanpakdee (119683)
A1 Arun Chumkaeo (11778329)
A1 Marius Gilbert (118241)
A1 Anuwat Wiratsudakul (119668)
YR 2021
K1 Ecology
K1 Science Policy
K1 Infectious Diseases
K1 Virology
K1 Environmental Sciences not elsewhere classified
K1 Biological Sciences not elsewhere classified
K1 study thus aimed
K1 past four decades
K1 last ten years
K1 explainable variables using
K1 cattle population density
K1 generalized additive models
K1 monthly rabies occurrences
K1 spatial risk areas
K1 cases gradually increased
K1 identified risk factors
K1 animal rabies cases
K1 spatial models
K1 risk factors
K1 risk areas
K1 rabies occurrences
K1 animal rabies
K1 slightly increased
K1 gradually declined
K1 rabies spread
K1 xlink ">
K1 temporal history
K1 surveillance system
K1 strategic plans
K1 secondary roads
K1 rigorously considered
K1 results indicate
K1 predictive maps
K1 highly distributed
K1 animals affected
JF https://figshare.com/articles/figure/The_predictive_power_of_the_monthly_model_and_test_sets_100_bootstraps_/17107727
LK http://dx.doi.org/https://doi.org/10.1371/journal.pntd.0009980.g004
DO https://doi.org/10.1371/journal.pntd.0009980.g004
SF ELIB - SuUB Bremen

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