A modelling framework for the quantification of the potential effects of climate change on dengue incidence: a multi-scenario approach - NU/LSHTM project

Title of PhD project / theme

A modelling framework for the quantification of the potential effects of climate change on dengue incidence: a multi-scenario approach

Supervisory team

Dr. Felipe J Colón-González, MSc PhD (Lead)
Infectious Disease Epidemiology, LSHTM

Dr. Rachel Lowe, PhD
Infectious Disease Epidemiology, LSHTM

Dr. Lina Madaniyazi, PhD
School of Tropical Medicine and Global Health, Nagasaki University

Prof. Masahiro Hashizume, MD PhD
Department of Global Health Policy, The University of Tokyo

Brief description of project / theme

There is an increasing demand from the public health sector for improved access to regional predictions of the burden of vector-borne diseases which are needed to understand and manage health risks, and to prepare for the potential changes in burden due to climate change. Quantifying the potential impacts of climate change on such diseases is crucial from a public health and planning perspective. Most research, however, focuses on the potential effects of climate change on malaria.

Dengue is most rapidly spreading vector-borne viral disease in the world, and it is endemic to over 100 countries. Currently, Latin America is the region with the highest dengue burden. Recent estimates suggest that every year, about 54 million cases occur in the region (Bhatt S, et al., 2013). Research indicates that limit global warming to 2°C could reduce dengue cases by almost three million cases per year towards the end of the century compared with a scenario where mean global temperature warms by 3.7°C (Colón-González et al., 2018). Moreover, limiting warming to about 1.5 °C would generate an additional annual drop in cases of about 0.5 million. Whilst several studies have quantified the potential impact of climate change on dengue in the region, most studies have focused on the climate suitability for the vector rather than on the suitability for disease transmission. Moreover, the interaction between representative concentration pathways (RCP) and shared socioeconomic pathways (SSP), and the effect of important factors such as population density and land-use change have been seldom explored.

This PhD aims to design a methodological framework for the quantification of the potential impacts of climate change on dengue incidence in Latin America considering the effects of vector-distribution, land-use change, and population density under a suite of scenarios based on the interactions between RCP and SSP. Latin America has been selected because it is currently the region of the world with the largest dengue burden. Brazil, Colombia, and Mexico will be used as study cases because they are three the countries with the largest dengue incidence in Latin America. They are also three of the countries with some of the longest publicly available records of dengue incidence in the region at the municipal level (Brazil: 2001-2017; Colombia: 2007-2018; Mexico: 2000-2019).

The project benefits from the use of state-of-the-art statistical methods, and the use of one of the longest sets of municipal dengue case time-series assembled for any country in the region. The student will be exposed to cutting-edge statistical methods on climate and health, and to epidemiological research design. As a member of the Centre for Mathematical Modelling of Infectious Diseases (CMMID), the Centre on Climate Change and Planetary Health (CCCPH), and the Planetary Health and Infectious Diseases group (PHID). The student will have access to world-class training and research environment to develop high-level research and quantitative skills.


  • Bhatt S, et al., 2013, The global distribution and burden of dengue, Nature 496:504–507
  • Colón-González et al., 2018, Limiting global-mean temperature increase to 1.5–2 °C could reduce the incidence and spatial spread of dengue fever in Latin America, PNAS, 115:6243-6248

The role of LSHTM and NU in this collaborative project

Prof Hashizume has an outstanding record in the quantification of the effects of climate on human health and contributes to multiple projects in this research area. Dr Colón-González’s research focuses on investigating the effects global environmental change on the spatiotemporal dynamics of climate-sensitive diseases.

Dr Lowe’s research involves understanding how environmental and socio-economic factors interact to determine the risk of disease transmission. This project offers an excellent opportunity to collaborate, and bring complementary skills to an interdisciplinary project. Both supervisors will work closely to train student and oversee the project to completion. An advisory committee will be identified at a later stage depending on the skillset of the candidate.

Particular prior educational requirements for a student undertaking this project

The student will require a quantitative background (e.g. MSc in epidemiology, Data science, environmental science, or medical statistics). Prior experience with spatio-temporal regression modelling, spatial data analysis, and entomology is preferred. Technical expertise with at least one computer coding language (ideally R) is highly desirable.

Skills we expect a student to develop/acquire whilst pursuing this project

The student will develop skills in research methods in epidemiology, statistical modelling of infectious diseases, Bayesian statistics, climate science, and study design. The student will also develop skills in scientific writing, critical reading of scientific literature, and science communication.