Quantifying spatio-temporal variation in malaria transmission in near elimination settings using individual level surveillance data

As countries move towards malaria elimination, tracking progress through quantifying changes in transmission over space and time is key. This information is necessary to effectively target resources to remaining ‘hotspots’ (high-risk locations) and ‘hotpops’ (high-risk populations) where transmission remains, decide if and when it is appropriate to scale back interventions, and to evaluate the success of existing interventions. However, as countries approach zero cases, it becomes difficult to measure transmission. Traditional metrics, such as the prevalence of parasites in the population, are no longer appropriate due to small numbers and increasingly focal distributions of cases over space and time. In order to address this, this thesis developed Bayesian network inference approaches to utilise information about the time and location of cases showing symptoms of malaria to jointly infer the likelihood that a) each observed case was linked to another by transmission and b) that a case was infected by an external, unobserved source. This information was used to calculate individual reproduction numbers for each reported case, or how many new cases of malaria are expected to have resulted from each case. In elimination settings, quantifying the distribution of individual reproduction numbers provides useful information about how quickly a disease may die out, and how the introduction of new cases through importation may affect ongoing transmission. These estimates were incorporated into additive regression models as well as geostatistical models to map how malaria transmission varied over space and time as well as considering timelines to elimination and the likelihood of resurgence of transmission once zero cases is achieved. This approach was applied to previously unanalysed individual-level datasets of malaria cases from China and El Salvador.Open Acces

Modelling the impact of larviciding on the population dynamics and biting rates of Simulium damnosum (s.l.): implications for vector control as a complementary strategy for onchocerciasis elimination in Africa

Background: In 2012, the World Health Organization set goals for the elimination of onchocerciasis transmission by 2020 in selected African countries. Epidemiological data and mathematical modelling have indicated that elimination may not be achieved with annual ivermectin distribution in all endemic foci. Complementary and alternative treatment strategies (ATS), including vector control, will be necessary. Implementation of vector control will require that the ecology and population dynamics of Simulium damnosum sensu lato be carefully considered. Methods: We adapted our previous SIMuliid POPulation dynamics (SIMPOP) model to explore the impact of larvicidal insecticides on S. damnosum (s.l.) biting rates in different ecological contexts and to identify how frequently and for how long vector control should be continued to sustain substantive reductions in vector biting. SIMPOP was fitted to data from large-scale aerial larviciding trials in savannah sites (Ghana) and small-scale ground larviciding trials in forest areas (Cameroon). The model was validated against independent data from Burkina Faso/Côte d’Ivoire (savannah) and Bioko (forest). Scenario analysis explored the effects of ecological and programmatic factors such as pre-control daily biting rate (DBR) and larviciding scheme design on reductions and resurgences in biting rates. Results: The estimated efficacy of large-scale aerial larviciding in the savannah was greater than that of ground-based larviciding in the forest. Small changes in larvicidal efficacy can have large impacts on intervention success. At 93% larvicidal efficacy (a realistic value based on field trials), 10 consecutive weekly larvicidal treatments would reduce DBRs by 96% (e.g. from 400 to 16 bites/person/day). At 70% efficacy, and for 10 weekly applications, the DBR would decrease by 67% (e.g. from 400 to 132 bites/person/day). Larviciding is more likely to succeed in areas with lower water temperatures and where blackfly species have longer gonotrophic cycles. Conclusions: Focal vector control can reduce vector biting rates in settings where a high larvicidal efficacy can be achieved and an appropriate duration and frequency of larviciding can be ensured. Future work linking SIMPOP with onchocerciasis transmission models will permit evaluation of the impact of combined anti-vectorial and anti-parasitic interventions on accelerating elimination of the disease

Serodynamics: a review of methods for epidemiological inference using serological data

The availability and diversity of serological data measuring antibody responses to infectious pathogens, accelerated in response to the SARS-CoV-2 pandemic, has enabled key insights into infectious disease dynamics and population health. Here, we present a review of analytical approaches and considerations for inference using serological data, highlighting the range of epidemiological and biological insights that are possible using appropriate mathematical and statistical models. This in-depth review focuses on methods to understand transmission dynamics and infer past exposures from serological data, referred to as serodynamics, though we note that such analyses often address complementary immunological questions. We first discuss key considerations for data processing and interpretation of raw serological data which are prerequisite for fitting serodynamical models. We then review a range of approaches for estimating epidemiological trends, ranging from classical serocatalytic models applied to binary serostatus data, to contemporary methods using full quantitative antibody measurements and immunological understanding to estimate if and when individuals have been previously infected. Here, we collate and synthesize these approaches within the context of a unifying framework for the overall data-generation process, consisting of key concepts including antibody kinetics, quantitative models to represent within-host and epidemic processes, and considerations for linking observed serological data to models. We close with a discussion of the types of methodological developments needed to meet the increasingly complex serological data becoming available that provide new avenues for scientific discovery and public health insights

serosim: An R package for simulating serological data arising from vaccination, epidemiological and antibody kinetics processes.

serosim is an open-source R package designed to aid inference from serological studies, by simulating data arising from user-specified vaccine and antibody kinetics processes using a random effects model. Serological data are used to assess population immunity by directly measuring individuals' antibody titers. They uncover locations and/or populations which are susceptible and provide evidence of past infection or vaccination to help inform public health measures and surveillance. Both serological data and new analytical techniques used to interpret them are increasingly widespread. This creates a need for tools to simulate serological studies and the processes underlying observed titer values, as this will enable researchers to identify best practices for serological study design, and provide a standardized framework to evaluate the performance of different inference methods. serosim allows users to specify and adjust model inputs representing underlying processes responsible for generating the observed titer values like time-varying patterns of infection and vaccination, population demography, immunity and antibody kinetics, and serological sampling design in order to best represent the population and disease system(s) of interest. This package will be useful for planning sampling design of future serological studies, understanding determinants of observed serological data, and validating the accuracy and power of new statistical methods

Changing epidemiology and challenges of malaria in China towards elimination

BackgroundHistorically, malaria had been a widespread disease in China. A national plan was launched in China in 2010, aiming to eliminate malaria by 2020. In 2017, no indigenous cases of malaria were detected in China for the first time. To provide evidence for precise surveillance and response to achieve elimination goal, a comprehensive study is needed to determine the changing epidemiology of malaria and the challenges towards elimination.MethodsUsing malaria surveillance data from 2011 to 2016, an integrated series of analyses was conducted to elucidate the changing epidemiological features of autochthonous and imported malaria, and the spatiotemporal patterns of malaria importation from endemic countries.ResultsFrom 2011 to 2016, a total of 21,062 malaria cases with 138 deaths were reported, including 91% were imported and 9% were autochthonous. The geographic distribution of local transmission have shrunk dramatically, but there were still more than 10 counties reporting autochthonous cases in 2013–2016, particularly in counties bordering with countries in South-East Asia. The importation from 68 origins countries had an increasing annual trend from Africa but decreasing importation from Southeast Asia. Four distinct communities have been identified in the importation networks with the destinations in China varied by origin and species.ConclusionsChina is on the verge of malaria elimination, but the residual transmission in border regions and the threats of importation from Africa and Southeast Asia are the key challenges to achieve and maintain malaria elimination. Efforts from China are also needed to help malaria control in origin countries and reduce the risk of introduced transmission

Replication Data for: Using Hawkes Processes to model imported and local malaria cases in near-elimination settings

This data set includes fits and simulations to recreate the figures in the paper &quot;Using Hawkes Processes to model imported and local malaria cases in near-elimination settings&quot;. The two original data sources have been published previously: China - Routledge I, Lai S, Battle KE, Ghani AC, Gomez-Rodriguez M, Gustafson KB, et al. Tracking progress towards malaria elimination in China: Individual-level estimates of transmission and its spatiotemporal variation using a diffusion network approach. PLOS Computational Biology. 2020;16(3):1&ndash;20. doi:10.1371/journal.pcbi.1007707. Eswatini - Reiner Jr RC, Menach AL, Kunene S, Ntshalintshali N, Hsiang MS, Perkins TA, et al. Mapping residual transmission for malaria elimination. elife. 2015;doi:10.7554/eLife.09520. Simulated data: The 10,000 simulations used for Fig 2 are the Eswatini simulations and we include the fits to our partial simulations used in Fig 3. Case studies: For our two case studies we include our Hawkes model fits (Fig 4) with an exponential and a Rayleigh kernel and our growth model fits. We also include our 10,000 simulations of each dataset used in Figs 5 and 6.</span