Additional file 1: Table A1. The meaning and value of covariates. Table A2. Demographic characteristics of scarlet fever in Sichuan Province, 2016–2019. Fig. A1. Time series of scarlet fever counts in 21 cities (/prefectures) of Sichuan province from 2016 to 2019. Figure A2. The fitted values for 21 cities (/prefectures) in Sichuan Province during 2016–2019. Fig. A3. The estimated multiplicative effect of seasonality on the endemic mean. Table A3. Model selection and comparison. Table A4. Model without spatial term. Table A5. The estimates of random effects. Fig. A4. Spatial connectivity weights. Fig. A5. The matrix of cities showing the connectivity weights
Time taken to reach the peak prevalence varies according to the household size distribution in the c...
Table S1. Moranâs I test statistic of meteorological measurements. Table S2 Q-statistic for meteor...
The periods of time for the respective local weather and global climate models for each study locati...
Additional file 2: Fig. S1. Gi* Cluster Map of COVID-19 incidence in mainland China. Fig. S2. Spatio...
Text S1. Data and methods. Table S1.Total and imported cases from 2012 to 2017. Table S2. Definition...
Table S1. Number of cases of influenza like illness (ILI) and influenza virus-positive specimens by ...
Additional file 2: Figure S1. An illustration on the change of number of cumulative cases (Nt) and t...
Text S1. Analysis of residual spatial dependence. Text S2. Model specification. Table S1. Number of ...
Goodness-of-fit of models. Table S1. Deviance explained for models using three different proxy varia...
Figure S1. Heatmaps and wavelet power spectrum of influenza virus. A, Time series of monthly age-spe...
Host distribution of 320 Chinese AIV sequences in Traditional Region, Economic Region, Economic Divi...
Text S1. Implementation of the empirical threshold method (ETM). Text S2. Implementation of the segm...
Methodology. 2.1 Data cleaning. Table S2.1.a. Number of observations and percentage of missing data....
The temporal dynamic of the proportion of scrub typhus patients by occupation groups in Guangzhou, 2...
ObjectiveOver the past decade, scarlet fever has caused a relatively high economic burden in various...
Time taken to reach the peak prevalence varies according to the household size distribution in the c...
Table S1. Moranâs I test statistic of meteorological measurements. Table S2 Q-statistic for meteor...
The periods of time for the respective local weather and global climate models for each study locati...
Additional file 2: Fig. S1. Gi* Cluster Map of COVID-19 incidence in mainland China. Fig. S2. Spatio...
Text S1. Data and methods. Table S1.Total and imported cases from 2012 to 2017. Table S2. Definition...
Table S1. Number of cases of influenza like illness (ILI) and influenza virus-positive specimens by ...
Additional file 2: Figure S1. An illustration on the change of number of cumulative cases (Nt) and t...
Text S1. Analysis of residual spatial dependence. Text S2. Model specification. Table S1. Number of ...
Goodness-of-fit of models. Table S1. Deviance explained for models using three different proxy varia...
Figure S1. Heatmaps and wavelet power spectrum of influenza virus. A, Time series of monthly age-spe...
Host distribution of 320 Chinese AIV sequences in Traditional Region, Economic Region, Economic Divi...
Text S1. Implementation of the empirical threshold method (ETM). Text S2. Implementation of the segm...
Methodology. 2.1 Data cleaning. Table S2.1.a. Number of observations and percentage of missing data....
The temporal dynamic of the proportion of scrub typhus patients by occupation groups in Guangzhou, 2...
ObjectiveOver the past decade, scarlet fever has caused a relatively high economic burden in various...
Time taken to reach the peak prevalence varies according to the household size distribution in the c...
Table S1. Moranâs I test statistic of meteorological measurements. Table S2 Q-statistic for meteor...
The periods of time for the respective local weather and global climate models for each study locati...