Table S1. An example of 25 years of dataset (training dataset the first 5 years + evaluation dataset the next 20 years) used in this study to evaluate outbreak detection algorithm and decision fusion methods (Baseline = 3 cases by days in average, Total number of outbreak cases injected =50 cases). The baseline (Column A) level of disease surveillance corresponding to an average of 3 cases declared by days in the system and the complete outbreak signal corresponding to a total of 50 cases according a shape of Norovirus outbreak injected (Column B) several time in the baseline. Column C represents the first day of the outbreak (1 = Start of the outbreak) and Column D all days considered as epidemic (=1). (XLSX 195 kb
International audienceBackgroundWhen outbreak detection algorithms (ODAs) are considered individuall...
Abstract Background When outbreak detection algorithms (ODAs) are considered individually, the task ...
Summary of predictor variables (Table S1-S5), example of spatial distribution of residuals and semi-...
Appendix to the main text, including further explanation of the models and algorithms used, descript...
Table S1. Initial clinical diagnoses and infectious source (derivation cohort). Table S2. 28-day mor...
Table S1 Frequency Distributions of the Number of Adjacencies Simulated on the Basis of 1 Million Ra...
Text S1. Data and methods. Table S1.Total and imported cases from 2012 to 2017. Table S2. Definition...
Table S1. Moranâs I test statistic of meteorological measurements. Table S2 Q-statistic for meteor...
Is Table S1 presenting diagnoses in patients admitted with sepsis by infection likelihood. Overview ...
Attack rates and relative risks per potential source farm. Number of cases, number of total inhabita...
Figure S1. Early case onset reports. Figure S2. Posterior parameter values 12th December 2018. Figur...
International audienceBackgroundWhen outbreak detection algorithms (ODAs) are considered individuall...
International audienceBackgroundWhen outbreak detection algorithms (ODAs) are considered individuall...
Due to the nature of many infectious agents, such as anthrax, symptoms may either take several days ...
Screenshot of COSARA central infection dashboard view. Time-graphs at the top of the page show the e...
International audienceBackgroundWhen outbreak detection algorithms (ODAs) are considered individuall...
Abstract Background When outbreak detection algorithms (ODAs) are considered individually, the task ...
Summary of predictor variables (Table S1-S5), example of spatial distribution of residuals and semi-...
Appendix to the main text, including further explanation of the models and algorithms used, descript...
Table S1. Initial clinical diagnoses and infectious source (derivation cohort). Table S2. 28-day mor...
Table S1 Frequency Distributions of the Number of Adjacencies Simulated on the Basis of 1 Million Ra...
Text S1. Data and methods. Table S1.Total and imported cases from 2012 to 2017. Table S2. Definition...
Table S1. Moranâs I test statistic of meteorological measurements. Table S2 Q-statistic for meteor...
Is Table S1 presenting diagnoses in patients admitted with sepsis by infection likelihood. Overview ...
Attack rates and relative risks per potential source farm. Number of cases, number of total inhabita...
Figure S1. Early case onset reports. Figure S2. Posterior parameter values 12th December 2018. Figur...
International audienceBackgroundWhen outbreak detection algorithms (ODAs) are considered individuall...
International audienceBackgroundWhen outbreak detection algorithms (ODAs) are considered individuall...
Due to the nature of many infectious agents, such as anthrax, symptoms may either take several days ...
Screenshot of COSARA central infection dashboard view. Time-graphs at the top of the page show the e...
International audienceBackgroundWhen outbreak detection algorithms (ODAs) are considered individuall...
Abstract Background When outbreak detection algorithms (ODAs) are considered individually, the task ...
Summary of predictor variables (Table S1-S5), example of spatial distribution of residuals and semi-...