Application of Functional Data Analysis to Identify Patterns of Malaria Incidence, to Guide Targeted Control Strategies

Abstract

We introduce an approach based on functional data analysis to identify patterns of malaria incidence to guide effective targeting of malaria control in a seasonal transmission area. Using functional data method, a smooth function (functional data or curve) was fitted from the time series of observed malaria incidence for each of 575 villages in west-central Senegal from 2008 to 2012. These 575 smooth functions were classified using hierarchical clustering (Ward’s method), and several different dissimilarity measures. Validity indices were used to determine the number of distinct temporal patterns of malaria incidence. Epidemiological indicators characterizing the resulting malaria incidence patterns were determined from the velocity and acceleration of their incidences over time. We identified three distinct patterns of malaria incidence: high-, intermediate-, and low-incidence patterns in respectively 2% (12/575), 17% (97/575), and 81% (466/575) of villages. Epidemiological indicators characterizing the fluctuations in malaria incidence showed that seasonal outbreaks started later, and ended earlier, in the low-incidence pattern. Functional data analysis can be used to identify patterns of malaria incidence, by considering their temporal dynamics. Epidemiological indicators derived from their velocities and accelerations, may guide to target control measures according to patterns.

Geo-epidemiology of malaria incidence in the Vhembe District to guide targeted elimination strategies, South-Africa, 2015-2018: a local resurgence

“…This analysis was based on an approach well described in [15]. Firstly, the weekly malaria incidence time series of each locality was determined over the study period.…”

“…Therefore, the introduction of the functional approach in this framework of epidemiology may be an alternative. While the statistical method choices were widely discussed in [15], one of the main advantages of using the functional approach is the application over a large number of geographical units and long periods of time where the data are aggregated in a very granular way. One of the limitations of this approach, for operational aspects, could be the dispersion of the localities of the same pattern over the geographical space.…”

“…Thus, clustering algorithms [14] can be used to identify broad types of temporal dynamics. A Senegalese study is the rst to use this approach to identify malaria patterns and to stratify malaria incidence level at village scale [15]. This suggests that this approach is applicable in the context of other endemic countries.…”

Malaria temporal dynamic clustering for surveillance and intervention planning

“…Based on our previous comparison of metrics in the context of malaria, DTW metric was the most appropriate. 14 Second, DTW algorithm performance is sensitive to sharp irregularities. 15 Malaria time series at village scale were sharp and noisy, and required smoothing.…”

Sub-national tailoring of seasonal malaria chemoprevention in Mali based on malaria surveillance and rainfall data

“…The WHO technical guidelines for malaria control recommend the implementation of targeted interventions in malaria endemic countries [ 4 ]. Targeting consists of tailoring interventions to the local epidemiological context to make better use of available financial resources [ 5 , 6 ]. Recommended interventions include community-based diagnostic and treatment, chemoprevention for children aged 3–59 months and pregnant women as well as vector control using long-lasting insecticidal nets (LLINs), indoor residual spraying (IRS) and larviciding [ 4 , 7 ].…”