Punam Amratia
Honorary Research Associate
PhD
punam.amratia@thekids.org.au
Punam Amratia is the Technical Lead of Malaria Atlas Project (MAP) Dar, located at Ifakara Health Institute, Dar es Salaam, Tanzania and has been a team member of MAP since 2018.
With over a decade of experience in malaria epidemiology, biostatistics, and geo-spatial modelling, Dr. Amratia has established herself as a leader in the field, contributing significantly to the understanding and mapping of malaria risk and burden.
Through her work, she has provided essential technical support to both malaria elimination settings using multi-metric approaches and WHO High Burden High Impact countries, leveraging household surveys and routine surveillance data to enhance malaria control and elimination strategies.
As a skilled educator, Dr. Amratia has developed and taught introductory data science courses at the University of Oxford and as part of the Carpentries and R-ladies, focusing on data handling, manipulation, and visualization.
She has also played a pivotal role in capacity strengthening in many malaria-endemic countries through the development of spatial training material and continues to support the learning of those interested in geostatistics and data science.
Education and Qualifications
- PhD from School of Forest Resources and Conservation (affiliate to Emerging Pathogens Institute)– University of Florida
- MSc in Modern Epidemiology – Imperial College London
Projects
Geospatial modelling for malaria risk stratification and intervention targeting for low-endemic countries
Geospatial modelling for malaria risk stratification and intervention targeting for high burden high impact countries
Endemic country capacity building and decentralization
Published research
Fine-scale maps of malaria incidence to inform risk stratification in Laos
Malaria risk maps are crucial for controlling and eliminating malaria by identifying areas of varying transmission risk. In the Greater Mekong Subregion, these maps guide interventions and resource allocation. This article focuses on analysing changes in malaria transmission and developing fine-scale risk maps using five years of routine surveillance data in Laos (2017-2021). The study employed data from 1160 geolocated health facilities in Laos, along with high-resolution environmental data.
Geospatial joint modeling of vector and parasite serology to microstratify malaria transmission
The World Health Organization identifies a strong surveillance system for malaria and its mosquito vector as an essential pillar of the malaria elimination agenda. Anopheles salivary antibodies are emerging biomarkers of exposure to mosquito bites that potentially overcome sensitivity and logistical constraints of traditional entomological surveys.
Malaria risk stratification in Lao PDR guides program planning in an elimination setting
Malaria in Lao People's Democratic Republic (Lao PDR) has declined rapidly over the last two decades, from 279,903 to 3926 (99%) cases between 2001 and 2021. Elimination of human malaria is an achievable goal and limited resources need to be targeted at remaining hotspots of transmission.
Reach and perceived effectiveness of a community-led active outreach postvention intervention for people bereaved by suicide
Postvention is a core component of suicide prevention strategies, internationally. However, the types of supports provided to people impacted by suicide vary widely. This study examines the perceived effectiveness of the Primary Care Navigator (PCN) model for people bereaved by suicide. The PCN model was implemented in response to a suicide cluster.
Spatial codistribution of HIV, tuberculosis and malaria in Ethiopia
HIV, tuberculosis (TB) and malaria are the three most important infectious diseases in Ethiopia, and sub-Saharan Africa. Understanding the spatial codistribution of these diseases is critical for designing geographically targeted and integrated disease control programmes. This study investigated the spatial overlap and drivers of HIV, TB and malaria prevalence in Ethiopia.
Mapping malaria by sharing spatial information between incidence and prevalence data sets
As malaria incidence decreases and more countries move towards elimination, maps of malaria risk in low-prevalence areas are increasingly needed. For low-burden areas, disaggregation regression models have been developed to estimate risk at high spatial resolution from routine surveillance reports aggregated by administrative unit polygons.
Indirect effects of the COVID-19 pandemic on malaria intervention coverage, morbidity, and mortality in Africa: a geospatial modelling analysis
Substantial progress has been made in reducing the burden of malaria in Africa since 2000, but those gains could be jeopardised if the COVID-19 pandemic affects the availability of key malaria control interventions. The aim of this study was to evaluate plausible effects on malaria incidence and mortality under different levels of disruption to malaria control.