The Methodology and Data Analysis Group, led by Dr. Claude Flamand, is dedicated to ensuring the application of robust methodologies and accurate data analysis techniques in research projects conducted within the Epidemiology and Public Health Unit, as well as across all units within IPC.
With a team composed of six members, we bring together a diverse range of expertise and skills in study design methodology, data management, social science, and both basic and advanced data analysis methods. This enables us to conduct in-depth analyses and provide valuable insights into various types of studies, including cross-sectional, longitudinal cohort studies, cluster randomized trials, case-control studies, and surveillance data analysis.
In addition to our research activities, we actively contribute to the training of IPC staff by providing mentorship and hands-on experience. We also host several Ph.D. and master’s students in the fields of epidemiology, biostatistics, and data science, offering them valuable opportunities to further their research skills and contribute to ongoing projects.
Risk assessment of transmission dynamics of arboviral and zoonotic diseases in Cambodia
Despite the growing impact of infectious diseases worldwide and the strengthening of surveillance systems, many gaps remain in our understanding of the mechanisms of transmission and maintenance of causative pathogens in exposed areas. Traditional epidemiological surveillance only provides information on symptomatic cases, leaving asymptomatic or medically unattended infections and the immune landscape at the population level unaccounted for. To complement routine surveillance, the detection of immune responses in serum has been used for many years to determine population infection levels for a wide range of pathogens. However, determining an individual’s infection status and when they become infected is a complex challenge that depends on various factors, including human, virological, and environmental interactions. This information is crucial to assess the risk of transmission, yet studying the immunological status of affected populations remains a challenging task that requires financial, human, and material resources that are rarely available in affected countries. Recent technological advances have transformed sero-epidemiology into a powerful tool that bridges the gap between epidemiology, mathematical modeling, and public health to support risk reduction strategies. The Cambodian interface represents an ideal opportunity to study pathogen transmission in community settings by investigating human-animal-environment interractions. We are developing several population-based serological surveys at different scales to estimate the burden of infection, reconstruct the history of spatiotemporal transmission dynamics, and map infection risks at high resolution. The conducted works will inform public health authorities and guide precision public health strategies to combat priority infectious diseases effectively.
Predicting dengue incidence in Cambodia
Dengue fever is an endemic disease in Cambodia, characterized by seasonal patterns and sporadic major outbreaks that typically occur during the rainy season from May to October. The scale and timing of these outbreaks are challenging to predict. In some instances, the high number of severe dengue cases can overwhelm pediatric hospitals and strain the healthcare system, posing potential challenges in delivering quality care. Providing appropriate supportive care is crucial for patients with severe dengue and can significantly reduce the fatality rate.
To address these challenges, we are collaborating with the National Center for Parasitology, Entomology, and Malaria Control (CNM) to predict dengue incidence and outbreaks. By analyzing routine surveillance data using various time-series models, our objective is to identify early indicators of dengue cases at the beginning of the season.
Through this innovative approach, our aim is to improve the decision-making process and enhance preparedness for potential dengue outbreaks. Timely and accurate predictions of dengue incidence can assist healthcare authorities and policymakers in making informed decisions regarding resource allocation, outbreak response, and preventive measures.
Blocking Malaria Transmission in Forest Vulnerable Populations through Forest Malaria Workers: A Key for Malaria Elimination in Cambodia
With the goal of achieving a malaria-free Greater Mekong Subregion (GMS) by 2030, Cambodia has set ambitious targets to eliminate malaria caused by P. falciparum by 2023 and all malaria species by 2025. Forests serve as the primary reservoir for malaria in most Southeast Asian countries, including Cambodia. However, our understanding of the epidemiology and transmission of malaria within forested areas remains limited, as most malaria studies have focused on non-forest settings.
To bridge this knowledge gap, the Epidemiology and Public Health Unit at Institut Pasteur du Cambodge has taken the lead in a malaria control project targeting forest goers (FGs) in high transmission areas of Cambodia. The project aims to identify the key determinants of malaria transmission in forest environments and evaluate an intervention strategy tailored to the unique context of forest settings. By conducting a comprehensive observation-intervention study and applying appropriate statistical methods, we aim to contribute to the knowledge base on malaria transmission in forested areas and evaluate the impact of our evidence-based intervention.
While demonstrating the effectiveness of specific interventions may pose challenges due to the overall decline in malaria cases nationwide, our project remains committed to evaluating the impact of our intervention strategy. We aim to support public health authorities in their ambitious goal of eliminating all human species of malaria by 2025. Through our research and collaboration, we strive to play a key role in blocking malaria transmission in forest vulnerable populations and ultimately contribute to the achievement of a malaria-free Cambodia.
