@article{fdi:010087638,
  title = {{M}alaria temporal dynamic clustering for surveillance and intervention planning},
  author = {{L}egendre, {E}. and {L}ehot, {L}. and {D}ieng, {S}. and {R}ebaudet, {S}. and {T}hu, {A}. {M}. and {R}ae, {J}. {D}. and {D}elmas, {G}. and {G}irond, {F}. and {H}erbreteau, {V}incent and {N}osten, {F}. and {L}andier, {J}ordi and {G}audart, {J}.},
  editor = {},
  language = {{ENG}},
  abstract = {{B}ackground: {T}argeting interventions where most needed and effective is crucial for public health. {M}alaria control and elimination strategies increasingly rely on stratification to guide surveillance, to allocate vector control campaigns, and to prioritize access to community-based early diagnosis and treatment ({EDT}). {W}e developed an original approach of dynamic clustering to improve local discrimination between heterogeneous malaria transmission settings. {M}ethods: {W}e analysed weekly malaria incidence records obtained from community-based {EDT} (malaria posts) in {K}aren/{K}ayin state, {M}yanmar. {W}e smoothed longitudinal incidence series over multiple seasons using functional transformation. {W}e regrouped village incidence series into clusters using a dynamic time warping clustering and compared them to the standard, 5-category annual incidence standard stratification. {R}esults: {W}e included 1115 villages from 2016 to 2020. {W}e identified eleven {P}. falciparum and {P}. vivax incidence clusters which differed by amplitude, trends and seasonality. {S}pecifically the 124 villages classified as "high transmission area" in the standard {P}. falciparum stratification belonged to the 11 distinct groups when accounting to inter-annual trends and intra-annual variations. {L}ikewise for {P}. vivax, 399 "high transmission" villages actually corresponded to the 11 distinct dynamics.{C}onclusion: {O}ur temporal dynamic clustering methodology is easy to implement and extracts more information than standard malaria stratification. {O}ur method exploits longitudinal surveillance data to distinguish local dynamics, such as increasing inter-annual trends or seasonal differences, providing key information for decision-making. {I}t is relevant to malaria strategies in other settings and to other diseases, especially when many countries deploy health information systems and collect increasing amounts of health outcome data.{F}unding: {T}he {B}ill & {M}elinda {G}ates {F}oundation, {T}he {G}lobal {F}und against {AIDS}, {T}uberculosis and {M}alaria (the {R}egional {A}rtemisinin {I}nitiative) and the {W}ellcome {T}rust funded the {METF} program.},
  keywords = {{S}easonal malaria ; {T}emporal dynamics ; {C}lustering ; {MYANMAR}},
  booktitle = {},
  journal = {{E}pidemics},
  volume = {43},
  numero = {},
  pages = {[9 p.]},
  ISSN = {1755-4365},
  year = {2023},
  DOI = {10.1016/j.epidem.2023.100682},
  URL = {https://www.documentation.ird.fr/hor/fdi:010087638},
}