Fig 1.
a) Nationwide mean annual incidence rate (cases/1,000 people/year) from 2001 to 2019 (left), b) the distribution of average incidence rate in each district over the study period (middle), and c) the corresponding names and incidence rate in each district (right). Source: Authors’ own map using data from https://data.humdata.org/dataset/sri-lanka-administrative-levels-0-4-boundaries.
Fig 2.
Classification of study period by disease distribution at each district.
Fig 3.
Classification of study areas by disease dynamics at each district.
Source: Authors’ own map using data from https://data.humdata.org/dataset/sri-lanka-administrative-levels-0-4-boundaries.
Fig 4.
Joining classification of study areas and study period by incidence rate at each district.
Table 1.
Risk factors of clinical leishmaniasis in different districts.
Fig 5.
Observed and model-predicted temporal changes in incidence rate in the seven districts with the highest case numbers.
Climate, neighbor, and carryover represent climatic, neighboring-district dispersal, and carryover effects, respectively. The results of goodness-of-fit of each model were presented in Table 1, predictions was only made with significantly fitted models.
Fig 6.
Observed and model-predicted leishmaniasis cases by division and year.
Source: Authors’ own map using data from https://data.humdata.org/dataset/sri-lanka-administrative-levels-0-4-boundaries.
Fig 7.
Observed vs. model-predicted total cases from 2015 to 2025.
Table 2.
Division-level predictive model summary.