Faster indicators of chikungunya incidence using Google searches

Data underlying:

Miller, S., Preis, T., Mizzi, G., Bastos, L. S., Gomes, M. F. d. C., Coelho, F. C., Codeço, C. T., & Moat, H. S. (2022). Faster indicators of chikungunya incidence using Google searches. PLOS Neglected Tropical Diseases, 16, e0010441. doi:10.1371/journal.pntd.0010441.

MillerEtAl_ChikungunyaCaseCountData.csv

This file contains data on weekly chikungunya case counts in the city of Rio de Janeiro, aggregated by the week in which the case was first diagnosed (the notification week) and the delay in number of weeks in entering the case in the surveillance system.

notification_week_commencing: the start date of the epidemiological week in which cases were notified 

notification_week: the epidemiological week in which cases were notified

delay_in_weeks: the delay in number of weeks in entering the cases in the surveillance system

case_count: the number of cases that were notified in the specified week with the specified delay in number of weeks 

MillerEtAl_Fig1A.csv

The data underlying Fig. 1A.

pct_entered: the percentage of cases notified in the specified epidemiological week that had been entered by the end of the week commencing 26 May 2019

notification_week_commencing: the start date of the epidemiological week in which cases were notified 

notified_cases: the number of cases notified in the specified epidemiological week

entered_cases: the number of cases notified in the specified epidemiological week and entered by the end of the week commencing 26 May 2019 

MillerEtAl_Fig1B.csv

The data underlying Fig. 1B.

pct_entered: the percentage of cases notified in the specified epidemiological week that had been entered by the end of the week commencing 21 July 2019

notification_week_commencing: the start date of the epidemiological week in which cases were notified 

notified_cases: the number of cases notified in the specified epidemiological week 

entered_cases: the number of cases notified in the specified epidemiological week and entered by the end of the week commencing 21 July 2019 

MillerEtAl_Fig1C.csv

The data underlying Fig. 1C.

pct_entered: the percentage of cases notified in the specified epidemiological week that had been entered by the end of the week commencing 15 September 2019

notification_week_commencing: the start date of the epidemiological week in which cases were notified 

notified_cases: the number of cases notified in the specified epidemiological week 

entered_cases: the number of cases notified in the specified epidemiological week and entered by the end of the week commencing 15 September 2019 

MillerEtAl_Fig2A.csv

The data underlying Fig. 2A.

notification_week_commencing: the start date of the epidemiological week in which cases were notified 

notified_cases: the number of cases notified in the specified epidemiological week 

entered_cases: the number of cases notified in the specified epidemiological week and entered by the end of the same week

MillerEtAl_Fig3FigS1A.csv

The data underlying Fig. 3 in the main text and Fig. A in S1 Appendix.

notification_week_commencing: the start date of the epidemiological week in which cases were notified 

notification_week: the epidemiological week in which cases were notified

notified_cases: the number of cases notified in the specified epidemiological week

baseline_mean: the baseline nowcasting model's mean estimate of the number of cases notified in the specified epidemiological week 

baseline_2.5: the lower bound of the baseline nowcasting model's 95% prediction interval for the number of cases notified in the specified epidemiological week

baseline_97.5: the upper bound of the baseline nowcasting model's 95% prediction interval for the number of cases notified in the specified epidemiological week

baseline_in_interval: whether the true number of notified cases for the specified epidemiological week fell within the baseline nowcasting model's 95% prediction interval

baseline_error: the difference between the baseline nowcasting model's mean estimate of the number of cases notified in the specified epidemiological week and the true number of notified cases

baseline_interval_width: the size of the baseline nowcasting model's 95% prediction interval for the number of cases notified in the specified epidemiological week

google_mean: the mean estimate of the number of cases notified in the specified epidemiological week produced by the nowcasting model using Google searches 

google_2.5: the lower bound of the 95% prediction interval for the number of cases notified in the specified epidemiological week produced by the nowcasting model using Google searches

google_97.5: the upper bound of the 95% prediction interval for the number of cases notified in the specified epidemiological week produced by the nowcasting model using Google searches

google_in_interval: whether the true number of notified cases for the specified epidemiological week fell within the 95% prediction interval produced by the nowcasting model using Google searches

google_error: the difference between the mean estimate of the number of cases notified in the specified epidemiological week produced by the nowcasting model using Google searches and the true number of notified cases

google_interval_width: the size of the 95% prediction interval for the number of cases notified in the specified epidemiological week produced by the nowcasting model using Google searches

heuristic: the heuristic model's estimate of the number of cases notified in the specified epidemiological week