Mapping residual transmission for malaria elimination

Reiner, Robert C.; Menach, Arnaud Le; Kunene, Simon; Ntshalintshali, Nyasatu; Hsiang, Michelle S.; Perkins, T. Alex; Greenhouse, Bryan; Tatem, Andrew J.; Cohen, Justin M.; Smith, David L.

doi:10.7554/elife.09520

Cited by 63 publications

(86 citation statements)

References 27 publications

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“…Understanding how transmission varies over time and space is critical to efforts to achieve and maintain elimination of infectious diseases such as malaria. Reconstructing transmission chains and estimating individual reproduction numbers has been used widely within epidemiological analysis 30,39,40 , but rarely used to study vector-borne or endemic diseases, albeit with a few notable exceptions 31,32 . Separately, similar problems have been approached within human social network analysis, through a family of approaches known as independent cascade models [33][34][35][36] .…”

Section: Discussionmentioning

confidence: 99%

“…For a given transmission tree T describing infection events linking cases and assuming the independent cascade model 33 , the (upper triangular) likelihood of observing our times of symptom onset is simply the product of all permissible pairwise transmission likelihoods in the tree 35 . Our exposition until this point is the same as that introduced by Wallinga and Teunis 29 and extended to a wide variety of contexts by others [30][31][32]43,44,50 . However, in contrast to previous methods based on Wallinga and Teunis we maximise the likelihood f ðtjGÞ conditional on an underlying G, a problem that is NP-hard 51 .…”

Section: Methodsmentioning

confidence: 99%

“…Individual level incidence data can be used to reconstruct the most likely pathways of transmission and estimate individual reproduction numbers, providing fine-scale insights into spatiotemporal transmission characteristics. While individual level surveillance data is often used in outbreak analysis of epidemic infections 29,30 , robust methods are rarely applied to vector-borne diseases such as malaria, with a few notable exceptions 17,31,32 .…”

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Estimating spatiotemporally varying malaria reproduction numbers in a near elimination setting

et al. 2018

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In 2016 the World Health Organization identified 21 countries that could eliminate malaria by 2020. Monitoring progress towards this goal requires tracking ongoing transmission. Here we develop methods that estimate individual reproduction numbers and their variation through time and space. Individual reproduction numbers, Rc, describe the state of transmission at a point in time and differ from mean reproduction numbers, which are averages of the number of people infected by a typical case. We assess elimination progress in El Salvador using data for confirmed cases of malaria from 2010 to 2016. Our results demonstrate that whilst the average number of secondary malaria cases was below one (0.61, 95% CI 0.55–0.65), individual reproduction numbers often exceeded one. We estimate a decline in Rc between 2010 and 2016. However we also show that if importation is maintained at the same rate, the country may not achieve malaria elimination by 2020.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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confidence: 99%

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Estimating spatiotemporally varying malaria reproduction numbers in a near elimination setting

et al. 2018

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“…Methods from outbreak analysis and network research have recently been developed and applied to quantify the transmission of malaria and other infectious diseases in near-elimination and epidemic settings [19][20][21]. In near elimination contexts with strong surveillance systems, traditional metrics of malaria such as parasite prevalence are not appropriate due to small numbers and extremely sparse and spatiotemporally heterogeneous distributions of infections.…”

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confidence: 99%

Tracking progress towards malaria elimination in China: Individual-level estimates of transmission and its spatiotemporal variation using a diffusion network approach

et al. 2020

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In order to monitor progress towards malaria elimination, it is crucial to be able to measure changes in spatio-temporal transmission. However, common metrics of malaria transmission such as parasite prevalence are under powered in elimination contexts. China has achieved major reductions in malaria incidence and is on track to eliminate, having reporting zero locally-acquired malaria cases in 2017 and 2018. Understanding the spatio-temporal pattern underlying this decline, especially the relationship between locally-acquired and imported cases, can inform efforts to maintain elimination and prevent re-emergence. This is particularly pertinent in Yunnan province, where the potential for local transmission is highest. Using a geo-located individual-level dataset of cases recorded in Yunnan province between 2011 and 2016, we introduce a novel Bayesian framework to model a latent diffusion process and estimate the joint likelihood of transmission between cases and the number of cases with unobserved sources of infection. This is used to estimate the case reproduction number, Rc. We use these estimates within spatio-temporal geostatistical models to map how transmission varied over time and space, estimate the timeline to elimination and the risk of resurgence. We estimate the mean Rc between 2011 and 2016 to be 0.171 (95% CI = 0.165, 0.178) for P. vivax cases and 0.089 (95% CI = 0.076, 0.103) for P. falciparum cases. From 2014 onwards, no cases were estimated to have a Rc value above one. An unobserved source of infection was estimated to be moderately likely (p>0.5) for 19/ 611 cases and high (p>0.8) for 2 cases, suggesting very high levels of case ascertainment. Our estimates suggest that, maintaining current intervention efforts, Yunnan is unlikely to experience sustained local transmission up to 2020. However, even with a mean PLOS COMPUTATIONAL BIOLOGY PLOS Computational Biology | https://doi. Data Availability Statement:The estimated maximum-a-posteriori Rc estimates and results of of 0.005 projected up to 2020, locally-acquired cases are possible due to high levels of importation. Author summaryAlthough malaria is still responsible for a great deal of death and illness in many parts of the world, many national control programmes have made great strides in controlling malaria and now are in a position to aim for elimination. However, in order to monitor progress towards elimination and plan interventions, it is crucial to measure malaria transmission and how it varies over space and time. However, traditional metrics used to measure malaria transmission are not suitable in elimination settings. China is one example of a country approaching elimination, with aims to eliminate the disease by 2020. Using a detailed individual level dataset of the times and locations of people showing symptoms of malaria, we use approaches adapted from the study of how information spreads through social networks to estimate the likelihood of transmission occurring between cases. This information is used to estimate how many people...

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“…In response, the group developed more refined models to capture important features of mosquito-borne infections, including mosquito life history, heterogeneous biting and fine-scale spatio-temporal variation in transmission. Applying these methods to malaria and dengue highlighted the impact of incorporating realistic mosquito biology and biting heterogeneities on control efforts Perkins, Garcia et al 2014;Reiner, Le Menach et al 2015).…”

Section: Seminal Reviews Identify Gaps In Disease Modelingmentioning

confidence: 99%

Fogarty International Center collaborative networks in infectious disease modeling: Lessons learnt in research and capacity building

et al. 2019

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A B S T R A C TDue to a combination of ecological, political, and demographic factors, the emergence of novel pathogens has been increasingly observed in animals and humans in recent decades. Enhancing global capacity to study and interpret infectious disease surveillance data, and to develop data-driven computational models to guide policy, represents one of the most cost-effective, and yet overlooked, ways to prepare for the next pandemic. Epidemiological and behavioral data from recent pandemics and historic scourges have provided rich opportunities for validation of computational models, while new sequencing technologies and the 'big data' revolution present new tools for studying the epidemiology of outbreaks in real time. For the past two decades, the Division of International Epidemiology and Population Studies (DIEPS) of the NIH Fogarty International Center has spearheaded two synergistic programs to better understand and devise control strategies for global infectious disease threats. The Multinational Influenza Seasonal Mortality Study (MISMS) has strengthened global capacity to study the epidemiology and evolutionary dynamics of influenza viruses in 80 countries by organizing international research activities and training workshops. The Research and Policy in Infectious Disease Dynamics (RAPIDD) program and its precursor activities has established a network of global experts in infectious disease modeling operating at the research-policy interface, with collaborators in 78 countries. These activities have provided evidence-based recommendations for disease control, including during large-scale outbreaks of pandemic influenza, Ebola and Zika virus. Together, these programs have coordinated international collaborative networks to advance the study of emerging disease threats and the field of computational epidemic modeling. A global community of researchers and policy-makers have used the tools and trainings developed by these programs to interpret infectious disease patterns in their countries, understand modeling concepts, and inform control policies. Here we reflect on the scientific achievements and lessons learnt from these programs (hindex = 106 for RAPIDD and 79 for MISMS), including the identification of outstanding researchers and fellows; funding flexibility for timely research workshops and working groups (particularly relative to more traditional investigator-based grant programs); emphasis on group activities such as large-scale modeling reviews, model comparisons, forecasting challenges and special journal issues; strong quality control with a light touch on outputs; and prominence of training, data-sharing, and joint publications.

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Mapping residual transmission for malaria elimination

Cited by 63 publications

References 27 publications

Estimating spatiotemporally varying malaria reproduction numbers in a near elimination setting

Estimating spatiotemporally varying malaria reproduction numbers in a near elimination setting

Tracking progress towards malaria elimination in China: Individual-level estimates of transmission and its spatiotemporal variation using a diffusion network approach

Fogarty International Center collaborative networks in infectious disease modeling: Lessons learnt in research and capacity building

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