Nick Golding’s research while affiliated with Curtin University and other places

Arboviruses transmitted mainly by Aedes (Stegomyia) aegypti and Ae. albopictus, including dengue, chikungunya, and Zika viruses, and yellow fever virus in urban settings, pose an escalating global threat. Existing risk maps, often hampered by surveillance biases, may underestimate or misrepresent the true distribution of these diseases and do not incorporate epidemiological similarities despite shared vector species. We address this by generating new global environmental suitability maps for Aedes-borne arboviruses using a multi-disease ecological niche model with a nested surveillance model fit to a dataset of over 21,000 occurrence points. This reveals a convergence in suitability around a common global distribution with recent spread of chikungunya and Zika closely aligning with areas suitable for dengue. We estimate that 5.66 (95% confidence interval 5.64-5.68) billion people live in areas suitable for dengue, chikungunya and Zika and 1.54 (1.53-1.54) billion people for yellow fever. We find large national and subnational differences in surveillance capabilities with higher income more accessible areas more likely to detect, diagnose and report viral diseases, which may have led to overestimation of risk in the United States and Europe. When combined with estimates of uncertainty, these suitability maps can be used by ministries of health to target limited surveillance and intervention resources in new strategies against these emerging threats.

Contact tracing is an important public health measure used to reduce transmission of infectious diseases. Contact tracers typically conduct telephone interviews with cases to identify contacts and direct them to quarantine, with the aim of preventing onward transmission. However, in situations where caseloads exceed the capacity of the public health system, timely interviews may not be feasible for all cases. Here we present a modelling framework for assessing the impact of different case interview prioritisation strategies on disease transmission. Our model is based on Australian contact tracing procedures and informed by contact tracing data on COVID-19 cases notified in Australia from 2020 to 2021. Our results demonstrate that last-in-first-out strategies (where cases with the most recent swab or notification dates are interviewed first) are more effective at reducing transmission than first-in-first-out strategies (where cases with the oldest swab or notification dates are interviewed first) or strategies with no explicit prioritisation. To maximise the public health benefit from a given case interview capacity, public health practitioners may consider our findings when designing case interview prioritisation protocols for outbreak response.

Background Since its inception in 2005, the President's Malaria Initiative (PMI) has played a major role in the reductions in malaria morbidity and mortality witnessed across Africa. With the status of PMI funding and operations currently uncertain, this study aimed to quantify the impact that a fully-functioning PMI would have on malaria cases and deaths in Africa during 2025. Methods We combined detailed spatio-temporal information on planned 2025 PMI and non-PMI malaria commodity procurement and distribution in Africa with spatio-temporal Bayesian models of intervention coverage and Plasmodium falciparum transmission and burden in Africa. By comparing coverage scenarios with and without planned PMI contributions we estimated the number of malaria cases and deaths PMI would avert in 2025. Findings We estimated that business-as-usual PMI contributions to vector control, seasonal chemoprevention, and routine malaria treatment in Africa would avert in 2025 14.9M (95% uncertainty interval 12.5M - 17.8M) malaria cases and 107,000 (71,000 - 166,000) deaths. This represents 12.6% (11.1 - 14.2%) and 39.0% (37.1 - 40.4%), respectively, of the total burden of malaria morbidity and mortality in PMI's focus geographies across 27 African countries. These estimates do not account for the additional impact of PMI-supported provision of diagnostics or severe case management commodities, nor preventive treatment for pregnant women which would add further to the averted burden. Interpretation PMI investment in supporting procurement and distribution of malaria control commodities would translate directly into millions of malaria cases averted and a hundred thousand lives saved across its focus geographies in Africa across 2025.

The implications of climate change for malaria eradication in the 21st century remain poorly resolved. Many studies have focussed on parasite and vector ecology in isolation, neglecting the interactions between climate, malaria control, and the socioeconomic environment, including the disruptive impact of extreme weather. Here we integrate 25 years of data on climate, malaria burden, control interventions, socioeconomic factors, and extreme weather events in Africa. Using a geotemporal model linked to an ensemble of climate projections under the Shared Socioeconomic Pathway 2-4.5 (SSP 2-4.5) scenario, we estimate the future impact of climate change on malaria burden in Africa, accounting for both ecological and disruptive effects. Our findings suggest climate change could lead to 123 million (projection range 49.5 million - 203 million) additional malaria cases and 532,000 (195,000 - 912,000) additional deaths in Africa between 2024 and 2050 under current control levels. Contrary to the prevailing focus on ecological mechanisms, extreme weather events emerge as the primary driver of increased risk, accounting for 79% (50-94%) of additional cases and 93% (70%-100%) of additional deaths. Most increases are due to intensification in existing endemic areas rather than range expansion, with significant regional variation in impact. These results highlight the urgent need for climate-resilient malaria control strategies and robust emergency response systems to safeguard progress toward malaria eradication in Africa.

Understanding and mapping the time to travel among locations is useful for many activities from urban planning to public health and myriad others. Here we present a software package — traveltime — written in and for the language R. traveltime enables a user to create a map of the motorised or walking travel time over an area of interest from a user-specified set of geographic coordinates. The result is a raster of the area of interest where the value in each cell is the lowest travel time in minutes to any of the specified locations. We envisage this software having diverse applications including: estimating sampling bias in species occurrence data, mapping electric vehicle charger accessibility, allocating public defibrillators, setting rehabilitation districts for stroke patients, or understanding access to agricultural processing facilities.

Quantifying the extent to which previous infections and vaccinations confer protection against future infection or disease outcomes is critical to managing the transmission and consequences of infectious diseases. We present a general statistical model for predicting the strength of protection conferred by different immunising exposures (numbers, types, and variants of both vaccines and infections), against multiple outcomes of interest, whilst accounting for immune waning. We predict immune protection against key clinical outcomes: developing symptoms, hospitalisation, and death. We also predict transmission-related outcomes: acquisition of infection and onward transmission in breakthrough infections. These enable quantification of the impact of immunity on population-level transmission dynamics. Our model calibrates the level of immune protection, drawing on both population-level data, such as vaccine effectiveness estimates, and neutralising antibody levels as a correlate of protection. This enables the model to learn realised immunity levels beyond those which can be predicted by antibody kinetics or other correlates alone. We demonstrate an application of the model for SARS-CoV-2, and predict the individual-level protective effectiveness conferred by natural infections with the Delta and the Omicron B.1.1.529 variants, and by the BioNTech-Pfizer (BNT162b2), Oxford-AstraZeneca (ChAdOx1), and 3rd-dose mRNA booster vaccines, against outcomes for both Delta and Omicron. We also demonstrate a use case of the model in late 2021 during the emergence of Omicron, showing how the model can be rapidly updated with emerging epidemiological data on multiple variants in the same population, to infer key immunogenicity and intrinsic transmissibility characteristics of the new variant, before these can be directly observed via vaccine effectiveness data. This model provided timely inference on rapidly evolving epidemic situations of significant concern during the early stages of the COVID-19 pandemic. The general nature of the model enables it to be used to support management of a range of infectious diseases.

Climatic conditions are a key determinant of malaria transmission intensity, through their impacts on both the parasite and its mosquito vectors. Mathematical models relating climatic conditions to malaria transmission can be used to develop spatial maps of climatic suitability for malaria. These maps underpin efforts to quantify the distribution and burden of malaria in humans, enabling improved monitoring and control. Previous work has developed mathematical models and global maps for the suitability of temperature for malaria transmission. In this paper, existing temperature-based models are extended to include two other important bioclimatic factors: humidity and rainfall. This model is combined with fine spatial resolution climatic data to produce a more biologically-realistic global map of climatic suitability for Plasmodium falciparum malaria. The climatic suitability index developed corresponds more closely than previous temperature suitability indices with the global distribution of P. falciparum malaria. There is weak agreement between the Malaria Atlas Project estimates of P. falciparum prevalence in Africa and the estimates of suitability solely based on temperature (Spearman Correlation coefficient of ρ=0.24$\rho = 0.24$). The addition of humidity and then rainfall improves the comparison (ρ=0.62$\rho = 0.62$ when humidity added; ρ=0.70$\rho =0.70$ when both humidity and rainfall added). By incorporating the impacts of humidity and rainfall, this model identifies arid regions that are not climatically suitable for transmission of P. falciparum malaria. Incorporation of this improved index of climatic suitability into geospatial models can improve global estimates of malaria prevalence and transmission intensity.

Disease surveillance data was critical in supporting public health decisions throughout the coronavirus disease 2019 (COVID-19) pandemic. At the same time, the unprecedented circumstances of the pandemic revealed many shortcomings of surveillance systems for viral respiratory pathogens. Strengthening of surveillance systems was identified as a priority for the recently established Australian Centre for Disease Control, which represents a critical opportunity to review pre-pandemic and pandemic surveillance practices, and to decide on future priorities, during both pandemic and inter-pandemic periods. On 20 October 2022, we ran a workshop with experts from the academic and government sectors who had contributed to the COVID-19 response in Australia on ‘The role of surveillance in epidemic response’, at the University of New South Wales, Sydney, Australia. Following the workshop, we developed five recommendations to strengthen respiratory virus surveillance systems in Australia, which we present here. Our recommendations are not intended to be exhaustive. We instead chose to focus on data types that are highly valuable yet typically overlooked by surveillance planners. Three of the recommendations focus on data collection activities that support the monitoring and prediction of disease impact and the effectiveness of interventions (what to measure) and two focus on surveillance methods and capabilities (how to measure). Implementation of our recommendations would enable more robust, timely, and impactful epidemic analysis.

Arboviruses transmitted mainly by Aedes (Stegomiya) aegypti and Ae. albopictus in urban settings, including dengue, chikungunya, Zika, and yellow fever viruses, pose an escalating global threat. Existing risk maps, often hampered by surveillance biases, underestimate or misrepresent the true distribution of these diseases and do not incorporate epidemiological similarities despite shared vector species. We address this by generating new global environmental suitability maps for Aedes-borne arboviruses using a multi-disease ecological niche model with a nested surveillance model fit to a dataset of over 36,000 occurrence points. This reveals a convergence in suitability around a common global distribution with recent spread of chikungunya and Zika closely aligning with areas suitable for dengue. We estimate that 5.69 (95% confidence interval 5.67-5.71) billion people live in areas suitable for dengue, chikungunya and Zika and 1.54 (1.53-1.54) billion people for yellow fever. We find large national and subnational differences in surveillance capabilities with richer more accessible areas more likely to report viral diseases, which may have led to overestimation of risk in the United States and Europe. When combined with estimates of uncertainty, these suitability maps can be used by ministries of health to target limited surveillance and intervention resources in new strategies against these emerging threats.