Data from: Early warning signals of malaria resurgence in Kericho, Kenya

Campaigns to eliminate infectious diseases could be greatly aided by methods for providing early warning signals of resurgence. Theory predicts that as a disease transmission system undergoes a transition from stability at the disease-free equilibrium to sustained transmission, it will exhibit characteristic behaviours known as critical slowing down, referring to the speed at which fluctuations in the number of cases are dampened, for instance the extinction of a local transmission chain after infection from an imported case. These phenomena include increases in several summary statistics, including lag-1 autocorrelation, variance, and the first difference of variance. Here, we report the first empirical test of this prediction during the resurgence of malaria in Kericho, Kenya. For 10 summary statistics, we measured the approach to criticality in a rolling window to quantify the size of effect and directions. Nine of the statistics increased as predicted and variance, the first difference of variance, autocovariance, lag-1 autocorrelation, and decay time returned early warning signals of critical slowing down based on permutation tests. These results show that time series of disease incidence collected through ordinary surveillance activities may exhibit characteristic signatures prior to an outbreak, a phenomenon that may be quite general among infectious disease systems. # Early warning signals of malaria resurgence in Kericho, Kenya [doi.org](doi.org) ## Description of the data and file structure This repository contains data discussed in the manuscript: Harris MJ, Hay SI, Drake JM. Early warning signals of malaria resurgence in Kericho, Kenya. Biol Lett. 2020 Mar;16(3):20190713. doi: 10.1098/rsbl.2019.0713. Epub 2020 Mar 18. PMID: 32183637; PMCID: PMC7115183. All analyses were conducted in RStudio using R version 3.5.1 ### Files and variables #### File: kericho.csv **Description:** Monthly malaria incidence in Kericho, Kenya between January 1965 and November 2002. The remaining files are all Rdata files. #### File: pvals **Description:** returns a vector of the p-values corresponding to all ten indicators (calculated based on Kendall's Tau). #### File: pvals\_rf **Description:** list of length 96 where the ith entry corresponds to early warning signal testing beginning in December 1981 and ending i months after April 1985. Each entry of this list is a vector with the same format as `pvals`. Calculated based on Kendall's Tau. #### File: cors **Description:** matrix with 10,001 rows and 10 columns, with the first 10,000 rows reporting correlation coefficients for all ten indicators for the corresponding null model and the final row (cors[10001,]) giving the correlation coefficients for the Kericho data (calculated using Kendall's Tau). #### File: pvals\_rf\_spearman **Description:** list of length 96 where the ith entry corresponds to early warning signal testing beginning in December 1981 and ending i months after April 1985. Each entry of this list is a vector with the same format as `pvals_spearman` (calculated using Spearman's Rho). #### File: pvals\_sa **Description:** returns a vector with the same format as pvals, where the ith entry corresponds to a notional month of critical transition i months since March 1992 and the jth entry corresponds to a bandwidth size of 34+j (1 ≤ i ≤ 25 ; 1 ≤ j ≤ 11) (calculated using Kendall's Tau) #### File: pvals\_sa\_spearman **Description:** returns a vector with the same format as pvals_spearman, where the ith entry corresponds to a notional month of critical transition i months since March 1992 and the jth entry corresponds to a bandwidth size of 34+j (1 ≤ i ≤ 25 ; 1 ≤ j ≤ 11). Calculated using Spearman's Rho. #### File: pvals\_tw **Description:** returns a vector with the same format as pvals, where the ith entry corresponds to a notional month of beginning of approach to criticality i months since November 1987 and the jth entry corresponds to a notional month of beginning of approach to criticality i months since March 1993 (1 ≤ i ≤ 11 ; 1 ≤ j ≤ 11). Calculated using Kendall's Tau. ## Code/software Code is available at [github.com](github.com) and can be run using R version 3.5.1. ## Access information Data was derived from the following sources: * The malaria inpatient data were supplied by the chief physician Dr Walter Odonde, Unilever Tea Kenya Ltd and updated to 2010 by Mr Geoffrey Kores, the Records Officer.