Spatiotemporal dynamics of disease transmission: Learning from COVID-19 data

Naleen Chaminda Ganegoda, Dipo Aldila, Karunia Putra Wijaya

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Even though many advancements have been materialized in the prevention and control of infectious diseases, mitigation is a challenging task. The COVID-19 pandemic has shown how critical the interconnections are, as person-to-person contact finally leads to the spread from country to country. Human mobility between spatial units such as cities, districts, and provinces, has been identified as a key influence. In this chapter, spatial autocorrelation is investigated via Moran's index in relation to confirmed cases of COVID-19 in Sri Lanka for a careful choice of study period. Random spatial patterns are evidently restraining clustered or dispersed patterns, indicating that the whole country should be scrutinized in control measures and not only the hot spots. Weight matrix that represents the connectivity of districts is designed based on the distance between the central locations of districts. A novel optimal strategy is implemented for decaying parameter and threshold distance to achieve somewhat consistent spatial variation. A risk map is produced using Moran scatters that illustrates crucial district borders to close in case of further spread. A full profile of Moran scatters is also given as a comprehensive illustration of spatiotemporal dynamics.

Original languageEnglish
Title of host publicationOne Health
Subtitle of host publicationHuman, Animal, and Environment Triad
Number of pages16
ISBN (Electronic)9781119867333
ISBN (Print)9781119867302
Publication statusPublished - 22 May 2023


  • COVID-19
  • Discrete optimization
  • Moran scatter plot
  • Moran's index
  • Risk maps
  • Spatial autocorrelation


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