Publications
118 results found
Ledien J, Cucunubá ZM, Parra-Henao G, et al., 2023, From serological surveys to disease burden: a modelling pipeline for Chagas disease., Philosophical Transactions of the Royal Society B: Biological Sciences, Vol: 378, Pages: 1-12, ISSN: 0962-8436
In 2012, the World Health Organization (WHO) set the elimination of Chagas disease intradomiciliary vectorial transmission as a goal by 2020. After a decade, some progress has been made, but the new 2021–2030 WHO roadmap has set even more ambitious targets. Innovative and robust modelling methods are required to monitor progress towards these goals. We present a modelling pipeline using local seroprevalence data to obtain national disease burden estimates by disease stage. Firstly, local seroprevalence information is used to estimate spatio-temporal trends in the Force-of-Infection (FoI). FoI estimates are then used to predict such trends across larger and fine-scale geographical areas. Finally, predicted FoI values are used to estimate disease burden based on a disease progression model. Using Colombia as a case study, we estimated that the number of infected people would reach 506 000 (95% credible interval (CrI) = 395 000–648 000) in 2020 with a 1.0% (95%CrI = 0.8–1.3%) prevalence in the general population and 2400 (95%CrI = 1900–3400) deaths (approx. 0.5% of those infected). The interplay between a decrease in infection exposure (FoI and relative proportion of acute cases) was overcompensated by a large increase in population size and gradual population ageing, leading to an increase in the absolute number of Chagas disease cases over time.This article is part of the theme issue ‘Challenges and opportunities in the fight against neglected tropical diseases: a decade from the London Declaration on NTDs’.
Thrift E, Nouvellet P, Mathews F, 2023, Plastic Entanglement Poses a Potential Hazard to European Hedgehogs Erinaceus europaeus in Great Britain., Animals (Basel), Vol: 13, ISSN: 2076-2615
A questionnaire to gather evidence on the plastic entanglement of the European hedgehog (Erinaceus europaeus) was sent to 160 wildlife rehabilitation centres in Great Britain. Fifty-four responses were received, and 184 individual admissions owing to plastic entanglement were reported. Death was the outcome for 46% (n = 86) of these cases. A high proportion of Britain's hedgehogs enter rehabilitation centres annually (approximately 5% of the national population and potentially 10% of the urban population), providing a robust basis for assessing the minimum impacts at a national level. We estimate that 4000-7000 hedgehog deaths per year are attributable to plastic, with the true rate likely being higher, since many entangled hedgehogs-in contrast to those involved in road traffic accidents-will not be found. Population modelling indicates that this excess mortality is sufficient to cause population declines. Although the scale of the impact is much lower than that attributable to traffic, it is nevertheless an additional pressure on a species that is already in decline and presents a significant welfare issue to a large number of individuals.
Kim Y, Leopardi S, Scaravelli D, et al., 2023, Transmission dynamics of lyssavirus in Myotis myotis: mechanistic modelling study based on longitudinal seroprevalence data., Proc Biol Sci, Vol: 290
We investigated the transmission dynamics of lyssavirus in Myotis myotis and Myotis blythii, using serological, virological, demographic and ecological data collected between 2015 and 2022 from two maternity colonies in northern Italian churches. Despite no lyssavirus detection in 556 bats sampled over 11 events by reverse transcription-polymerase chain reaction (RT-PCR), 36.3% of 837 bats sampled over 27 events showed neutralizing antibodies to European bat lyssavirus 1, with a significant increase in summers. By fitting sets of mechanistic models to seroprevalence data, we investigated factors that influenced lyssavirus transmission within and between years. Five models were selected as a group of final models: in one model, a proportion of exposed bats (median model estimate: 5.8%) became infectious and died while the other exposed bats recovered with immunity without becoming infectious; in the other four models, all exposed bats became infectious and recovered with immunity. The final models supported that the two colonies experienced seasonal outbreaks driven by: (i) immunity loss particularly during hibernation, (ii) density-dependent transmission, and (iii) a high transmission rate after synchronous birthing. These findings highlight the importance of understanding ecological factors, including colony size and synchronous birthing timing, and potential infection heterogeneities to enable more robust assessments of lyssavirus spillover risk.
Kim Y, Donnelly CA, Nouvellet P, 2023, Drivers of SARS-CoV-2 testing behaviour: a modelling study using nationwide testing data in England., Nat Commun, Vol: 14
During the COVID-19 pandemic, national testing programmes were conducted worldwide on unprecedented scales. While testing behaviour is generally recognised as dynamic and complex, current literature demonstrating and quantifying such relationships is scarce, despite its importance for infectious disease surveillance and control. Here, we characterise the impacts of SARS-CoV-2 transmission, disease susceptibility/severity, risk perception, and public health measures on SARS-CoV-2 PCR testing behaviour in England over 20 months of the pandemic, by linking testing trends to underlying epidemic trends and contextual meta-data within a systematic conceptual framework. The best-fitting model describing SARS-CoV-2 PCR testing behaviour explained close to 80% of the total deviance in NHS test data. Testing behaviour showed complex associations with factors reflecting transmission level, disease susceptibility/severity (e.g. age, dominant variant, and vaccination), public health measures (e.g. testing strategies and lockdown), and associated changes in risk perception, varying throughout the pandemic and differing between infected and non-infected people.
Wardle J, Bhatia S, Kraemer MUG, et al., 2023, Gaps in mobility data and implications for modelling epidemic spread: a scoping review and simulation study, Epidemics: the journal of infectious disease dynamics, Vol: 42, Pages: 1-11, ISSN: 1755-4365
Reliable estimates of human mobility are important for understanding the spatial spread of infectious diseases and the effective targeting of control measures. However, when modelling infectious disease dynamics, data on human mobility at an appropriate temporal or spatial resolution are not always available, leading to the common use of model-derived mobility proxies. In this study we reviewed the different data sources and mobility models that have been used to characterise human movement in Africa. We then conducted a simulation study to better understand the implications of using human mobility proxies when predicting the spatial spread and dynamics of infectious diseases.We found major gaps in the availability of empirical measures of human mobility in Africa, leading to mobility proxies being used in place of data. Empirical data on subnational mobility were only available for 17/54 countries, and in most instances, these data characterised long-term movement patterns, which were unsuitable for modelling the spread of pathogens with short generation times (time between infection of a case and their infector). Results from our simulation study demonstrated that using mobility proxies can have a substantial impact on the predicted epidemic dynamics, with complex and non-intuitive biases. In particular, the predicted times and order of epidemic invasion, and the time of epidemic peak in different locations can be underestimated or overestimated, depending on the types of proxies used and the country of interest.Our work underscores the need for regularly updated empirical measures of population movement within and between countries to aid the prevention and control of infectious disease outbreaks. At the same time, there is a need to establish an evidence base to help understand which types of mobility data are most appropriate for describing the spread of emerging infectious diseases in different settings.
Nash RK, Cori A, Nouvellet P, 2022, Estimating the epidemic reproduction number from temporally aggregated incidence data: a statistical modelling approach and software tool
<jats:sec><jats:title>Background</jats:title><jats:p>The time-varying reproduction number (R<jats:sub>t</jats:sub>) is an important measure of epidemic transmissibility; it can directly inform policy decisions and the optimisation of control measures. EpiEstim is a widely used software tool that uses case incidence and the serial interval (SI, time between symptoms in a case and their infector) to estimate R<jats:sub>t</jats:sub>in real-time. The incidence and the SI distribution must be provided at the same temporal resolution, which limits the applicability of EpiEstim and other similar methods, e.g. for pathogens with a mean SI shorter than the frequency of incidence reporting.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We use an expectation-maximisation algorithm to reconstruct daily incidence from temporally aggregated data, from which R<jats:sub>t</jats:sub>can then be estimated using EpiEstim. We assess the validity of our method using an extensive simulation study and apply it to COVID-19 and influenza data. The method is implemented in the opensource R package EpiEstim.</jats:p></jats:sec><jats:sec><jats:title>Findings</jats:title><jats:p>For all datasets, the influence of intra-weekly variability in reported data was mitigated by using aggregated weekly data. R<jats:sub>t</jats:sub>estimated on weekly sliding windows using incidence reconstructed from weekly data was strongly correlated with estimates from the original daily data. The simulation study revealed that R<jats:sub>t</jats:sub>was well estimated in all scenarios and regardless of the temporal aggregation of the data. In the presence of weekend effects, R<jats:sub>t</jats:sub>estimates from reconstructed data were more successful at recovering the true value of R<jats:sub>t</jats:sub>than those
Hayes S, Lushasi K, Sambo M, et al., 2022, Understanding the incidence and timing of rabies cases in domestic animals and wildlife in south-east Tanzania in the presence of widespread domestic dog vaccination campaigns, VETERINARY RESEARCH, Vol: 53, ISSN: 0928-4249
Charniga K, Cucunuba Z, Walteros DM, et al., 2022, Estimating Zika virus attack rates and risk of Zika virus-associated neurological complications in Colombian capital cities with a Bayesian model, Royal Society Open Science, Vol: 9, ISSN: 2054-5703
Zika virus (ZIKV) is a mosquito-borne pathogen that caused a major epidemic in the Americas in 2015–2017. Although the majority of ZIKV infections are asymptomatic, the virus has been associated with congenital birth defects and neurological complications (NC) in adults. We combined multiple data sources to improve estimates of ZIKV infection attack rates (IARs), reporting rates of Zika virus disease (ZVD) and the risk of ZIKV-associated NC for 28 capital cities in Colombia. ZVD surveillance data were combined with post-epidemic seroprevalence data and a dataset on ZIKV-associated NC in a Bayesian hierarchical model. We found substantial heterogeneity in ZIKV IARs across cities. The overall estimated ZIKV IAR across the 28 cities was 0.38 (95% CrI: 0.17–0.92). The estimated ZVD reporting rate was 0.013 (95% CrI: 0.004–0.024), and 0.51 (95% CrI: 0.17–0.92) cases of ZIKV-associated NC were estimated to be reported per 10 000 ZIKV infections. When we assumed the same ZIKV IAR across sex or age group, we found important spatial heterogeneities in ZVD reporting rates and the risk of being reported as a ZVD case with NC. Our results highlight how additional data sources can be used to overcome biases in surveillance data and estimate key epidemiological parameters.
Bracher J, Wolffram D, Deuschel J, et al., 2022, National and subnational short-term forecasting of COVID-19 in Germany and Poland during early 2021., Commun Med (Lond), Vol: 2
BACKGROUND: During the COVID-19 pandemic there has been a strong interest in forecasts of the short-term development of epidemiological indicators to inform decision makers. In this study we evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland for the period from January through April 2021. METHODS: We evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland. These were issued by 15 different forecasting models, run by independent research teams. Moreover, we study the performance of combined ensemble forecasts. Evaluation of probabilistic forecasts is based on proper scoring rules, along with interval coverage proportions to assess calibration. The presented work is part of a pre-registered evaluation study. RESULTS: We find that many, though not all, models outperform a simple baseline model up to four weeks ahead for the considered targets. Ensemble methods show very good relative performance. The addressed time period is characterized by rather stable non-pharmaceutical interventions in both countries, making short-term predictions more straightforward than in previous periods. However, major trend changes in reported cases, like the rebound in cases due to the rise of the B.1.1.7 (Alpha) variant in March 2021, prove challenging to predict. CONCLUSIONS: Multi-model approaches can help to improve the performance of epidemiological forecasts. However, while death numbers can be predicted with some success based on current case and hospitalization data, predictability of case numbers remains low beyond quite short time horizons. Additional data sources including sequencing and mobility data, which were not extensively used in the present study, may help to improve performance.
Ledien J, Cucunuba ZM, Parra-Henao G, et al., 2022, Linear and machine learning modelling for spatiotemporal disease predictions: force-of-infection of chagas disease, PLoS Neglected Tropical Diseases, Vol: 16, Pages: 1-19, ISSN: 1935-2727
BackgroundChagas disease is a long-lasting disease with a prolonged asymptomatic period. Cumulative indices of infection such as prevalence do not shed light on the current epidemiological situation, as they integrate infection over long periods. Instead, metrics such as the Force-of-Infection (FoI) provide information about the rate at which susceptible people become infected and permit sharper inference about temporal changes in infection rates. FoI is estimated by fitting (catalytic) models to available age-stratified serological (ground-truth) data. Predictive FoI modelling frameworks are then used to understand spatial and temporal trends indicative of heterogeneity in transmission and changes effected by control interventions. Ideally, these frameworks should be able to propagate uncertainty and handle spatiotemporal issues.Methodology/principal findingsWe compare three methods in their ability to propagate uncertainty and provide reliable estimates of FoI for Chagas disease in Colombia as a case study: two Machine Learning (ML) methods (Boosted Regression Trees (BRT) and Random Forest (RF)), and a Linear Model (LM) framework that we had developed previously. Our analyses show consistent results between the three modelling methods under scrutiny. The predictors (explanatory variables) selected, as well as the location of the most uncertain FoI values, were coherent across frameworks. RF was faster than BRT and LM, and provided estimates with fewer extreme values when extrapolating to areas where no ground-truth data were available. However, BRT and RF were less efficient at propagating uncertainty.Conclusions/significanceThe choice of FoI predictive models will depend on the objectives of the analysis. ML methods will help characterise the mean behaviour of the estimates, while LM will provide insight into the uncertainty surrounding such estimates. Our approach can be extended to the modelling of FoI patterns in other Chagas disease-endemic countries and to o
Nash RK, Nouvellet P, Cori A, 2022, Real-time estimation of the epidemic reproduction number: Scoping review of the applications and challenges, PLOS Digital Health, Vol: 1, Pages: e0000052-e0000052, ISSN: 2767-3170
The time-varying reproduction number (Rt) is an important measure of transmissibility during outbreaks. Estimating whether and how rapidly an outbreak is growing (Rt > 1) or declining (Rt < 1) can inform the design, monitoring and adjustment of control measures in real-time. We use a popular R package for Rt estimation, EpiEstim, as a case study to evaluate the contexts in which Rt estimation methods have been used and identify unmet needs which would enable broader applicability of these methods in real-time. A scoping review, complemented by a small EpiEstim user survey, highlight issues with the current approaches, including the quality of input incidence data, the inability to account for geographical factors, and other methodological issues. We summarise the methods and software developed to tackle the problems identified, but conclude that significant gaps remain which should be addressed to enable easier, more robust and applicable estimation of Rt during epidemics.
Wardle J, Bhatia S, Kraemer MUG, et al., 2022, Gaps in mobility data and implications for modelling epidemic spread: a scoping review and simulation study
<jats:p>Reliable estimates of human mobility are important for understanding the spatial spread of infectious diseases and the effective targeting of control measures. However, when modelling infectious disease dynamics, data on human mobility at an appropriate temporal or spatial resolution are not always available, leading to the common use of model-derived mobility proxies. In this study we reviewed the different data sources and mobility models that have been used to characterise human movement in Africa. We then conducted a simulation study to better understand the implications of using human mobility proxies when predicting the spatial spread and dynamics of infectious diseases.We found major gaps in the availability of empirical measures of human mobility in Africa, leading to mobility proxies being used in place of data. Empirical data on subnational mobility were only available for 17/54 countries, and, in most instances, these data characterised long-term movement patterns, which were unsuitable for modelling the spread of pathogens with short generation times (time between infection of a case and their infector). Results from our simulation study demonstrated that using mobility proxies can have a substantial impact on the predicted epidemic dynamics, with complex and non-intuitive biases. In particular, the predicted times and order of epidemic invasion, and the time of epidemic peak in different locations can be underestimated or overestimated, depending on the types of proxies used and the country of interest.Our work underscores the need for regularly updated empirical measures of population movement within and between countries to aid the prevention and control of infectious disease outbreaks. At the same time, there is a need to establish an evidence base to help understand which types of mobility data are most appropriate for describing the spread of emerging infectious diseases in different settings.</jats:p>
Ledien J, Cucunuba ZM, Parra-Henao G, et al., 2022, Spatiotemporal variations in exposure: Chagas disease in Colombia as a case study, BMC Medical Research Methodology, Vol: 22, Pages: 1-12, ISSN: 1471-2288
Age-stratified serosurvey data are often used to understand spatiotemporal trends in disease incidence and exposure through estimating the Force-of-Infection (FoI). Typically, median or mean FoI estimates are used as the response variable in predictive models, often overlooking the uncertainty in estimated FoI values when fitting models and evaluating their predictive ability. To assess how this uncertainty impact predictions, we compared three approaches with three levels of uncertainty integration. We propose a performance indicator to assess how predictions reflect initial uncertainty.In Colombia, 76 serosurveys (1980–2014) conducted at municipality level provided age-stratified Chagas disease prevalence data. The yearly FoI was estimated at the serosurvey level using a time-varying catalytic model. Environmental, demographic and entomological predictors were used to fit and predict the FoI at municipality level from 1980 to 2010 across Colombia.A stratified bootstrap method was used to fit the models without temporal autocorrelation at the serosurvey level. The predictive ability of each model was evaluated to select the best-fit models within urban, rural and (Amerindian) indigenous settings. Model averaging, with the 10 best-fit models identified, was used to generate predictions.Our analysis shows a risk of overconfidence in model predictions when median estimates of FoI alone are used to fit and evaluate models, failing to account for uncertainty in FoI estimates. Our proposed methodology fully propagates uncertainty in the estimated FoI onto the generated predictions, providing realistic assessments of both central tendency and current uncertainty surrounding exposure to Chagas disease.
Bhatia S, Wardle J, Nash RK, et al., 2021, A generic method and software to estimate the transmission advantage of pathogen variants in real-time : SARS-CoV-2 as a case-study
<jats:title>Abstract</jats:title><jats:p>Recent months have demonstrated that emerging variants may set back the global COVID-19 response. The ability to rapidly assess the threat of new variants in real-time is critical for timely optimisation of control strategies.</jats:p><jats:p>We extend the EpiEstim R package, designed to estimate the time-varying reproduction number (<jats:italic>R</jats:italic><jats:sub><jats:italic>t</jats:italic></jats:sub>), to estimate in real-time the effective transmission advantage of a new variant compared to a reference variant. Our method can combine information across multiple locations and over time and was validated using an extensive simulation study, designed to mimic a variety of real-time epidemic contexts.</jats:p><jats:p>We estimate that the SARS-CoV-2 Alpha variant is 1.46 (95% Credible Interval 1.44-1.47) and 1.29, (95% CrI 1.29-1.30) times more transmissible than the wild type, using data from England and France respectively. We further estimate that Beta and Gamma combined are 1.25 (95% CrI 1.24-1.27) times more transmissible than the wildtype (France data). All results are in line with previous estimates from literature, but could have been obtained earlier and more easily with our off-the-shelf open-source tool.</jats:p><jats:p>Our tool can be used as an important first step towards quantifying the threat of new variants in real-time. Given the popularity of EpiEstim, this extension will likely be used widely to monitor the co-circulation and/or emergence of multiple variants of infectious pathogens.</jats:p><jats:sec><jats:title>Significance Statement</jats:title><jats:p>Early assessment of the transmissibility of new variants of an infectious pathogen is critical for anticipating their impact and designing appropriate interventions. However, this often requires complex and bespoke analyses relying
Bhatia S, Wardle J, Nash R, et al., 2021, Report 47: A generic method and software to estimate the transmission advantage of pathogen variants in real-time : SARS-CoV-2 as a case-study
Recent months have demonstrated that emerging variants may set back the global COVID-19 response.The ability to rapidly assess the threat of new variants in real-time is critical for timely optimisation ofcontrol strategies.We extend the EpiEstim R package, designed to estimate the time-varying reproduction number (Rt),to estimate in real-time the e ective transmission advantage of a new variant compared to a referencevariant. Our method can combine information across multiple locations and over time and was validatedusing an extensive simulation study, designed to mimic a variety of real-time epidemic contexts.We estimate that the SARS-CoV-2 Alpha variant is 1.46 (95% Credible Interval 1.44-1.47) and 1.29,(95% CrI 1.29-1.30) times more transmissible than the wild type, using data from England and Francerespectively. We further estimate that Beta and Gamma combined are 1.25 (95% CrI 1.24-1.27) timesmore transmissible than the wildtype (France data). All results are in line with previous estimates fromliterature, but could have been obtained earlier and more easily with our o -the-shelf open-source tool.Our tool can be used as an important rst step towards quantifying the threat of new variants inreal-time. Given the popularity of EpiEstim, this extension will likely be used widely to monitor theco-circulation and/or emergence of multiple variants of infectious pathogens.
Lushasi K, Hayes S, Ferguson EA, et al., 2021, Reservoir dynamics of rabies in Southeast Tanzania and the roles of cross-species transmission and domestic dog vaccination, Journal of Applied Ecology, Vol: 58, Pages: 2673-2685, ISSN: 0021-8901
1) Understanding the role of different species in the transmission of multi-host pathogens, such as rabies virus, is vital for effective control strategies. Across most of sub-Saharan Africa domestic dogs (Canis familiaris) are considered the reservoir for rabies, but the role of wildlife has been long debated. Here we explore the multi-host transmission dynamics of rabies across southeast Tanzania. 2) Between January 2011 and July 2019 data on probable rabies cases were collected in the regions of Lindi and Mtwara. Hospital records of animal-bite patients presenting to healthcare facilities were used as sentinels for animal contact tracing. The timing, location and species of probable rabid animals was used to reconstruct transmission trees to infer who infected whom and the relative frequencies of within-and between-species transmission. 3) During the study, 688 probable human rabies exposures were identified, resulting in 47 deaths. Of these exposures, 389 were from domestic dogs (56.5%) and 262 from jackals (38.1%). Over the same period 549 probable animal rabies cases were traced: 303 in domestic dogs (55.2%) and 221 in jackals (40.3%). 4) Although dog-to-dog transmission was most commonly inferred (40.5% of transmission events), a third of inferred events involved wildlife-to-wildlife transmission (32.6%) and evidence suggested some sustained transmission chains within jackal populations. 5) A steady decline in probable rabies cases in both humans and animals coincided with the implementation of widespread domestic dog vaccination during the first six years of the study. Following the lapse of this programme dog rabies cases began to increase in one of the northernmost districts. 6) Synthesis and applications: in southeast Tanzania, despite a relatively high incidence of rabies in wildlife and evidence of wildlife-to-wildlife transmission, domestic dogs remain essential to the reservoir of infection. Continued dog vaccination alongside improved surveillance
Bracher J, Wolffram D, Deuschel J, et al., 2021, National and subnational short-term forecasting of COVID-19 in Germany and Poland during early 2021
<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>During the COVID-19 pandemic there has been a strong interest in forecasts of the short-term development of epidemiological indicators to inform decision makers. In this study we evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland for the period from January through April 2021.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland. These were issued by 15 different forecasting models, run by independent research teams. Moreover, we study the performance of combined ensemble forecasts. Evaluation of probabilistic forecasts is based on proper scoring rules, along with interval coverage proportions to assess forecast calibration. The presented work is part of a pre-registered evaluation study and covers the period from January through April 2021.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>We find that many, though not all, models outperform a simple baseline model up to four weeks ahead for the considered targets. Ensemble methods (i.e., combinations of different available forecasts) show very good relative performance. The addressed time period is characterized by rather stable non-pharmaceutical interventions in both countries, making short-term predictions more straightforward than in previous periods. However, major trend changes in reported cases, like the rebound in cases due to the rise of the B.1.1.7 (alpha) variant in March 2021, prove challenging to predict.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Multi-model approaches can help to improve the performance of epidemiological forecasts. Howeve
Desai A, Nouvellet P, Bhatia S, et al., 2021, Data journalism and the COVID-19 pandemic: opportunities and challenges, The Lancet Digital Health, Vol: 3, Pages: e619-e621, ISSN: 2589-7500
Forna A, Dorigatti I, Nouvellet P, et al., 2021, Comparison of machine learning methods for estimating case fatality ratios: an Ebola outbreak simulation study, PLoS One, Vol: 16, ISSN: 1932-6203
BackgroundMachine learning (ML) algorithms are now increasingly used in infectious disease epidemiology. Epidemiologists should understand how ML algorithms behave within the context of outbreak data where missingness of data is almost ubiquitous.MethodsUsing simulated data, we use a ML algorithmic framework to evaluate data imputation performance and the resulting case fatality ratio (CFR) estimates, focusing on the scale and type of data missingness (i.e., missing completely at random—MCAR, missing at random—MAR, or missing not at random—MNAR).ResultsAcross ML methods, dataset sizes and proportions of training data used, the area under the receiver operating characteristic curve decreased by 7% (median, range: 1%–16%) when missingness was increased from 10% to 40%. Overall reduction in CFR bias for MAR across methods, proportion of missingness, outbreak size and proportion of training data was 0.5% (median, range: 0%–11%).ConclusionML methods could reduce bias and increase the precision in CFR estimates at low levels of missingness. However, no method is robust to high percentages of missingness. Thus, a datacentric approach is recommended in outbreak settings—patient survival outcome data should be prioritised for collection and random-sample follow-ups should be implemented to ascertain missing outcomes.
Bhatia S, Parag K, Wardle J, et al., 2021, Global predictions of short- to medium-term COVID-19 transmission trends : a retrospective assessment
<jats:title>Abstract</jats:title> <jats:p>From 8th March to 29th November 2020, we produced weekly estimates of SARS-CoV-2 transmissibility and forecasts of deaths due to COVID-19 for 81 countries with evidence of sustained transmission. We also developed a novel heuristic to combine weekly estimates of transmissibility to produce forecasts over a 4-week horizon. We evaluated the robustness of the forecasts using relative error, coverage probability, and comparisons with null models. During the 39-week period covered by this study, both the short- and medium-term forecasts captured well the epidemic trajectory across different waves of COVID-19 infections with small relative errors over the forecast horizon. The model was well calibrated with 56.3\% and 45.6\% of the observations lying in the 50\% Credible Interval in 1-week and 4-week ahead forecasts respectively. We could accurately characterise the overall phase of the epidemic up to 4-weeks ahead in 84.9\% of country-days. The medium-term forecasts can be used in conjunction with the short-term forecasts of COVID-19 mortality as a useful planning tool as countries continue to relax public health measures.</jats:p>
Bhatia S, Parag KV, Wardle J, et al., 2021, Global predictions of short- to medium-term COVID-19 transmission trends : a retrospective assessment
<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>As of July 2021, more than 180,000,000 cases of COVID-19 have been reported across the world, with more than 4 million deaths. Mathematical modelling and forecasting efforts have been widely used to inform policy-making and to create situational awareness.</jats:p></jats:sec><jats:sec><jats:title>Methods and Findings</jats:title><jats:p>From 8<jats:sup>th</jats:sup> March to 29<jats:sup>th</jats:sup> November 2020, we produced weekly estimates of SARS-CoV-2 transmissibility and forecasts of deaths due to COVID-19 for countries with evidence of sustained transmission. The estimates and forecasts were based on an ensemble model comprising of three models that were calibrated using only the reported number of COVID-19 cases and deaths in each country. We also developed a novel heuristic to combine weekly estimates of transmissibility and potential changes in population immunity due to infection to produce forecasts over a 4-week horizon. We evaluated the robustness of the forecasts using relative error, coverage probability, and comparisons with null models.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>During the 39-week period covered by this study, we produced short- and medium-term forecasts for 81 countries. Both the short- and medium-term forecasts captured well the epidemic trajectory across different waves of COVID-19 infections with small relative errors over the forecast horizon. The model was well calibrated with 56.3% and 45.6% of the observations lying in the 50% Credible Interval in 1-week and 4-week ahead forecasts respectively. We could accurately characterise the overall phase of the epidemic up to 4-weeks ahead in 84.9% of country-days. The medium-term forecasts can be used in conjunction with the short-term fo
Gold S, Donnelly C, Woodroffe R, et al., 2021, Modelling the influence of naturally acquired immunity from subclinical infection on outbreak dynamics and persistence of rabies in domestic dogs, PLoS Neglected Tropical Diseases, Vol: 15, Pages: 1-19, ISSN: 1935-2727
A number of mathematical models have been developed for canine rabies to explore dynamics and inform control strategies. A common assumption of these models is that naturally acquired immunity plays no role in rabies dynamics. However, empirical studies have detected rabies-specific antibodies in healthy, unvaccinated domestic dogs, potentially due to immunizing, non-lethal exposure. We developed a stochastic model for canine rabies, parameterised for Laikipia County, Kenya, to explore the implications of different scenarios for naturally acquired immunity to rabies in domestic dogs. Simulating these scenarios using a non-spatial model indicated that low levels of immunity can act to limit rabies incidence and prevent depletion of the domestic dog population, increasing the probability of disease persistence. However, incorporating spatial structure and human response to high rabies incidence allowed the virus to persist in the absence of immunity. While low levels of immunity therefore had limited influence under a more realistic approximation of rabies dynamics, high rates of exposure leading to immunizing non-lethal exposure were required to produce population-level seroprevalences comparable with those reported in empirical studies. False positives and/or spatial variation may contribute to high empirical seroprevalences. However, if high seroprevalences are related to high exposure rates, these findings support the need for high vaccination coverage to effectively control this disease.
Charniga K, Cucunuba ZM, Mercado M, et al., 2021, Spatial and temporal invasion dynamics of the 2014-2017 Zika and chikungunya epidemics in Colombia, PLOS COMPUTATIONAL BIOLOGY, Vol: 17, ISSN: 1553-734X
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Charniga K, Cucunuba Z, Walteros DM, et al., 2021, Descriptive analysis of surveillance data for Zika virus disease and Zika virus-associated neurological complications in Colombia, 2015-2017, PLoS One, Vol: 16, Pages: 1-16, ISSN: 1932-6203
Zika virus (ZIKV) is a mosquito-borne pathogen that recently caused a major epidemic in the Americas. Although the majority of ZIKV infections are asymptomatic, the virus has been associated with birth defects in fetuses and newborns of infected mothers as well as neurological complications in adults. We performed a descriptive analysis on approximately 106,000 suspected and laboratory-confirmed cases of Zika virus disease (ZVD) that were reported during the 2015–2017 epidemic in Colombia. We also analyzed a dataset containing patients with neurological complications and recent febrile illness compatible with ZVD. Females had higher cumulative incidence of ZVD than males. Compared to the general population, cases were more likely to be reported in young adults (20 to 39 years of age). We estimated the cumulative incidence of ZVD in pregnant females at 3,120 reported cases per 100,000 population (95% CI: 3,077–3,164), which was considerably higher than the incidence in both males and non-pregnant females. ZVD cases were reported in all 32 departments. Four-hundred and eighteen patients suffered from ZIKV-associated neurological complications, of which 85% were diagnosed with Guillain-Barré syndrome. The median age of ZIKV cases with neurological complications was 12 years older than that of ZVD cases. ZIKV-associated neurological complications increased with age, and the highest incidence was reported among individuals aged 75 and older. Even though neurological complications and deaths due to ZIKV were rare in this epidemic, better risk communication is needed for people living in or traveling to ZIKV-affected areas.
Djaafara A, Whittaker C, Watson OJ, et al., 2021, Using syndromic measures of mortality to capture the dynamics of COVID-19 in Java, Indonesia in the context of vaccination roll-out, BMC Medicine, Vol: 19, ISSN: 1741-7015
Background: As in many countries, quantifying COVID-19 spread in Indonesia remains challenging due to testing limitations. In Java, non-pharmaceutical interventions (NPIs) were implemented throughout 2020. However, as a vaccination campaign launches, cases and deaths are rising across the island. Methods: We used modelling to explore the extent to which data on burials in Jakarta using strict COVID-19 protocols (C19P) provide additional insight into the transmissibility of the disease, epidemic trajectory, and the impact of NPIs. We assess how implementation of NPIs in early 2021 will shape the epidemic during the period of likely vaccine roll-out. Results: C19P burial data in Jakarta suggest a death toll approximately 3.3 times higher than reported. Transmission estimates using these data suggest earlier, larger, and more sustained impact of NPIs. Measures to reduce sub-national spread, particularly during Ramadan, substantially mitigated spread to more vulnerable rural areas. Given current trajectory, daily cases and deaths are likely to increase in most regions as the vaccine is rolled-out. Transmission may peak in early 2021 in Jakarta if current levels of control are maintained. However, relaxation of control measures is likely to lead to a subsequent resurgence in the absence of an effective vaccination campaign. Conclusions: Syndromic measures of mortality provide a more complete picture of COVID-19 severity upon which to base decision-making. The high potential impact of the vaccine in Java is attributable to reductions in transmission to date and dependent on these being maintained. Increases in control in the relatively short-term will likely yield large, synergistic increases in vaccine impact.
Bhatia S, Lassmann B, Cohn E, et al., 2021, Using digital surveillance tools for near real-time mapping of the risk of infectious disease spread, npj Digital Medicine, Vol: 4, ISSN: 2398-6352
Data from digital disease surveillance tools such as ProMED and HealthMap can complement the field surveillance during ongoing outbreaks. Our aim was to investigate the use of data collected through ProMED and HealthMap in real-time outbreak analysis. We developed a flexible statistical model to quantify spatial heterogeneity in the risk of spread of an outbreak and to forecast short term incidence trends. The model was applied retrospectively to data collected by ProMED and HealthMap during the 2013–2016 West African Ebola epidemic and for comparison, to WHO data. Using ProMED and HealthMap data, the model was able to robustly quantify the risk of disease spread 1–4 weeks in advance and for countries at risk of case importations, quantify where this risk comes from. Our study highlights that ProMED and HealthMap data could be used in real-time to quantify the spatial heterogeneity in risk of spread of an outbreak.
Nouvellet P, Bhatia S, Cori A, et al., 2021, Reduction in mobility and COVID-19 transmission, Nature Communications, Vol: 12, ISSN: 2041-1723
In response to the COVID-19 pandemic, countries have sought to control SARS-CoV-2 transmission by restricting population movement through social distancing interventions, thus reducing the number of contacts.Mobility data represent an important proxy measure of social distancing, and here, we characterise the relationship between transmission and mobility for 52 countries around the world.Transmission significantly decreased with the initial reduction in mobility in 73% of the countries analysed, but we found evidence of decoupling of transmission and mobility following the relaxation of strict control measures for 80% of countries. For the majority of countries, mobility explained a substantial proportion of the variation in transmissibility (median adjusted R-squared: 48%, interquartile range - IQR - across countries [27-77%]). Where a change in the relationship occurred, predictive ability decreased after the relaxation; from a median adjusted R-squared of 74% (IQR across countries [49-91%]) pre-relaxation, to a median adjusted R-squared of 30% (IQR across countries [12-48%]) post-relaxation.In countries with a clear relationship between mobility and transmission both before and after strict control measures were relaxed, mobility was associated with lower transmission rates after control measures were relaxed indicating that the beneficial effects of ongoing social distancing behaviours were substantial.
Djaafara BA, Whittaker C, Watson OJ, et al., 2021, Quantifying the Dynamics of COVID-19 Burden and Impact of Interventions in Java, Indonesia, Publisher: Elsevier BV
Fu H, Wang H, Xi X, et al., 2021, A database for the epidemic trends and control measures during the first wave of COVID-19 in mainland China, International Journal of Infectious Diseases, Vol: 102, Pages: 463-471, ISSN: 1201-9712
Objectives: This data collation effort aims to provide a comprehensive database to describe the epidemic trends and responses during the first wave of coronavirus disease 2019 (COVID-19)across main provinces in China. Methods: From mid-January to March 2020, we extracted publicly available data on the spread and control of COVID-19 from 31 provincial health authorities and major media outlets in mainland China. Based on these data, we conducted a descriptive analysis of the epidemics in the six most-affected provinces. Results: School closures, travel restrictions, community-level lockdown, and contact tracing were introduced concurrently around late January but subsequent epidemic trends were different across provinces. Compared to Hubei, the other five most-affected provinces reported a lower crude case fatality ratio and proportion of critical and severe hospitalised cases. From March 2020, as local transmission of COVID-19 declined, switching the focus of measures to testing and quarantine of inbound travellers could help to sustain the control of the epidemic. Conclusions: Aggregated indicators of case notifications and severity distributions are essential for monitoring an epidemic. A publicly available database with these indicators and information on control measures provides useful source for exploring further research and policy planning for response to the COVID-19 epidemic.
Unwin H, Mishra S, Bradley V, et al., 2020, State-level tracking of COVID-19 in the United States, Nature Communications, Vol: 11, Pages: 1-9, ISSN: 2041-1723
As of 1st June 2020, the US Centers for Disease Control and Prevention reported 104,232 confirmed or probable COVID-19-related deaths in the US. This was more than twice the number of deaths reported in the next most severely impacted country. We jointly model the US epidemic at the state-level, using publicly available deathdata within a Bayesian hierarchical semi-mechanistic framework. For each state, we estimate the number of individuals that have been infected, the number of individuals that are currently infectious and the time-varying reproduction number (the average number of secondary infections caused by an infected person). We use changes in mobility to capture the impact that non-pharmaceutical interventions and other behaviour changes have on therate of transmission of SARS-CoV-2. We estimate thatRtwas only below one in 23 states on 1st June. We also estimate that 3.7% [3.4%-4.0%] of the total population of the US had been infected, with wide variation between states, and approximately 0.01% of the population was infectious. We demonstrate good 3 week model forecasts of deaths with low error and good coverage of our credible intervals.
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