Malaria temporal dynamic clustering for surveillance and intervention planning.

Area of Science:

• Epidemiology
• Public Health
• Parasitology

Background:

• Effective malaria control requires precise targeting of interventions.
• Stratification guides surveillance, vector control, and early diagnosis and treatment (EDT).
• Existing stratification methods may not fully capture local transmission heterogeneity.

Purpose of the Study:

• To develop and evaluate a novel dynamic clustering approach for improved malaria transmission stratification.
• To enhance local discrimination between heterogeneous malaria transmission settings.
• To provide more informative data for public health decision-making.

Main Methods:

• Weekly malaria incidence data from community-based EDT in Karen/Kayin state, Myanmar (2016-2020) were analyzed.
• Longitudinal incidence series were smoothed and clustered using dynamic time warping.
• The novel clustering was compared against standard annual incidence stratification.

Main Results:

• Eleven distinct clusters for Plasmodium falciparum and P. vivax incidence were identified, differing in amplitude, trends, and seasonality.
• The dynamic clustering revealed complex transmission dynamics within areas previously classified as uniformly "high transmission."
• This method provided a more nuanced understanding of local malaria transmission patterns.

Conclusions:

• The temporal dynamic clustering methodology offers enhanced information extraction compared to standard malaria stratification.
• The approach effectively utilizes longitudinal surveillance data to identify local transmission dynamics.
• This method is applicable to malaria control strategies in diverse settings and other diseases with abundant health data.