Predicting malaria transmissibility in a near elimination setting and identifying key drivers for malaria elimination

As international communities embark on global malaria elimination, transmission facilitated by frequent human migration through the importation of malaria cases, will pose a major challenge to control efforts, particularly in the face of emergent anti-malarial resistant parasites. It is crucial to investigate the role of malaria importation on local malaria transmission. Therefore, this project aims to develop a statistical method to predict how imported malaria drives the local transmission in the malaria elimination settings, which are characterized by few cases. 6 We will use malaria data from Bhutan from 2017 to 2021 for this project. Bhutan is one of the countries pursuing the global goal of malaria elimination and provides a good case study. For example, in 2020, there were 49 cases in Bhutan. Of which, 29 cases were imported cases. We expect firstly, to understand how the importation of malaria drives the local transmission. Secondly, we will use this case study to develop statistical methods for predicting and understanding the role of importation in local transmission in other malaria eliminating countries. This information will support governments in adopting local specific elimination efforts. Thereby, supporting the global agenda of malaria elimination.

Malaria surveillance data from the Bhutan National Vector-borne Diseases Control Program will be used for this study. The study period will be from 2017 to 2021. The national surveillance data captured all the cases diagnosed and treated in Bhutan. Since there is no private practice in Bhutan. Patients with fever in seven historically malaria endemics districts bordering India undergo a mandatory blood test for malaria with microscopy. All positive cases are treated for specific species following the national treatment guideline and case based investigation is undertaken as per the WHO guidelines.16 The following information will be extracted for this study: travel history in the last two weeks (for malaria classification into imported, local, and introduced), latitude and longitude of the house of the patient, date of symptoms onset, date of diagnosis, and date of treatment. The principal investigator (KW) already has data. The data used are the times of symptoms onset for all identified cases of malaria infection, and the times are ordered to form the nodes in a potential transmission network that we are going to find out. The likelihood of transmission is calculated based on the distribution of serial interval (which is the time between a given case showing symptoms and a subsequent case showing symptoms) and the distribution is usually unobservable and requires some assumed distribution (e.g., shifted Rayleigh distribution used in Routledge et al.17 to reasonably capture our understanding of the transmission process. For a transmission network, the reproduction number ???? for an individual case (referred as to the individual reproduction number) is given by the mean number of connected subsequent cases (directly caused by the individual case) and is calculated by sum of normalized likelihood (probability) of those connected subsequent cases. Then we will extend the method by Routledge et al. by allowing the parameters of the Figure 1: Flow chart of the method for estimating the most likely transmission network and individual reproduction number. 9 distribution of serial interval to be a function of those factors, in order to assess how the transmission pattern and individual reproduction number are influenced by various climate factors such as temperature and humidity (which information is available in the Bhutan data) and using the significant variable identified by Wangdi et al.18 Study timeline This study will be conducted over 12-months period with the proposed timeline outline below: Jan – Jun 2023: Bhutan data analysis and prediction Jul – Sep 2023: Manuscript writing Oct – Dec 2023: Submission of reports.

Surveillance

Nov 2022 — Oct 2023

$15,000

Australia

Australian Centre for Research Excellence in Malaria Elimination (ACREME)