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Subramanian S, Maheswari RU, Prabavathy G, Khan MA, Brindha B, Srividya A, Kumar A, Rahi M, Nightingale ES, Medley GF, Cameron MM, Roy N, Jambulingam P. Modelling spatiotemporal patterns of visceral leishmaniasis incidence in two endemic states in India using environment, bioclimatic and demographic data, 2013-2022. PLoS Negl Trop Dis 2024; 18:e0011946. [PMID: 38315725 PMCID: PMC10868833 DOI: 10.1371/journal.pntd.0011946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 02/15/2024] [Accepted: 01/26/2024] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND As of 2021, the National Kala-azar Elimination Programme (NKAEP) in India has achieved visceral leishmaniasis (VL) elimination (<1 case / 10,000 population/year per block) in 625 of the 633 endemic blocks (subdistricts) in four states. The programme needs to sustain this achievement and target interventions in the remaining blocks to achieve the WHO 2030 target of VL elimination as a public health problem. An effective tool to analyse programme data and predict/ forecast the spatial and temporal trends of VL incidence, elimination threshold, and risk of resurgence will be of use to the programme management at this juncture. METHODOLOGY/PRINCIPAL FINDINGS We employed spatiotemporal models incorporating environment, climatic and demographic factors as covariates to describe monthly VL cases for 8-years (2013-2020) in 491 and 27 endemic and non-endemic blocks of Bihar and Jharkhand states. We fitted 37 models of spatial, temporal, and spatiotemporal interaction random effects with covariates to monthly VL cases for 6-years (2013-2018, training data) using Bayesian inference via Integrated Nested Laplace Approximation (INLA) approach. The best-fitting model was selected based on deviance information criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC) and was validated with monthly cases for 2019-2020 (test data). The model could describe observed spatial and temporal patterns of VL incidence in the two states having widely differing incidence trajectories, with >93% and 99% coverage probability (proportion of observations falling inside 95% Bayesian credible interval for the predicted number of VL cases per month) during the training and testing periods. PIT (probability integral transform) histograms confirmed consistency between prediction and observation for the test period. Forecasting for 2021-2023 showed that the annual VL incidence is likely to exceed elimination threshold in 16-18 blocks in 4 districts of Jharkhand and 33-38 blocks in 10 districts of Bihar. The risk of VL in non-endemic neighbouring blocks of both Bihar and Jharkhand are less than 0.5 during the training and test periods, and for 2021-2023, the probability that the risk greater than 1 is negligible (P<0.1). Fitted model showed that VL occurrence was positively associated with mean temperature, minimum temperature, enhanced vegetation index, precipitation, and isothermality, and negatively with maximum temperature, land surface temperature, soil moisture and population density. CONCLUSIONS/SIGNIFICANCE The spatiotemporal model incorporating environmental, bioclimatic, and demographic factors demonstrated that the KAMIS database of the national programmme can be used for block level predictions of long-term spatial and temporal trends in VL incidence and risk of outbreak / resurgence in endemic and non-endemic settings. The database integrated with the modelling framework and a dashboard facility can facilitate such analysis and predictions. This could aid the programme to monitor progress of VL elimination at least one-year ahead, assess risk of resurgence or outbreak in post-elimination settings, and implement timely and targeted interventions or preventive measures so that the NKAEP meet the target of achieving elimination by 2030.
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Affiliation(s)
| | | | | | | | - Balan Brindha
- ICMR-Vector Control Research Centre, Indira Nagar, Puducherry, India
| | | | - Ashwani Kumar
- ICMR-Vector Control Research Centre, Indira Nagar, Puducherry, India
| | - Manju Rahi
- ICMR-Vector Control Research Centre, Indira Nagar, Puducherry, India
- Division of Epidemiology and Communicable Diseases, Indian Council of Medical Research, New Delhi, India
| | - Emily S Nightingale
- Centre for Mathematical Modelling of Infectious Disease and Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Graham F Medley
- Centre for Mathematical Modelling of Infectious Disease and Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Mary M Cameron
- Department of Disease Control, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Nupur Roy
- National Centre for Vector-Borne Diseases Control, Ministry of Health and Family Welfare, Government of India, New Delhi
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Nightingale ES, Feasey HRA, Khundi M, Soko RN, Burke RM, Nliwasa M, Twabi H, Mpunga JA, Fielding K, MacPherson P, Corbett EL. Community-level variation in TB testing history in Blantyre, Malawi. Int J Tuberc Lung Dis 2024; 28:99-105. [PMID: 38303035 DOI: 10.5588/ijtld.23.0213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024] Open
Abstract
SETTING: Equitable access to TB testing is vital for achieving global diagnosis and treatment targets, but access to diagnostic services is often worse in poorer communities. The SCALE (Sustainable Community-wide Active case-finding for Lung hEalth) survey estimated TB prevalence in Blantyre City, Malawi, and recorded previous engagement with TB services.OBJECTIVE: To explore local variation in the prevalence of ever-testing for TB in Blantyre and investigate potential socio-economic drivers.DESIGN: We fit a mixed-effects model to self-reported prior TB testing from survey participants across 72 neighbourhood clusters, adjusted for sex, age and HIV status and with cluster-level random intercepts. We then evaluated to what extent cluster-level variation was explained by two alternate poverty indicators.RESULTS: We observed substantial variation between clusters in previous TB testing, with little correlation between neighbouring clusters. Individuals residing in less affluent households, on average, had lower odds of having undergone prior testing. However, adjusting for poverty did not explain the cluster-level variations observed.CONCLUSION: Despite a decade of increased active case-finding efforts, access to TB testing is inconsistent across the population of Blantyre. This likely reflects health inequities that also apply to TB testing in many other settings, and motivates collection and analysis of TB testing data to identify the drivers behind these inequities.
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Affiliation(s)
| | - H R A Feasey
- London School of Hygiene & Tropical Medicine, London, UK;, Malawi Liverpool Wellcome Trust Clinical Research Programme, Blantyre
| | - M Khundi
- African Institute for Development Policy, Lilongwe
| | - R N Soko
- Malawi Liverpool Wellcome Trust Clinical Research Programme, Blantyre
| | - R M Burke
- London School of Hygiene & Tropical Medicine, London, UK;, Malawi Liverpool Wellcome Trust Clinical Research Programme, Blantyre
| | - M Nliwasa
- Kamuzu University of Health Sciences, Blantyre, Malawi
| | - H Twabi
- Kamuzu University of Health Sciences, Blantyre, Malawi;, University of Liverpool, Liverpool, UK
| | - J A Mpunga
- National TB and Leprosy Elimination Programme, Ministry of Health, Lilongwe, Malawi
| | - K Fielding
- London School of Hygiene & Tropical Medicine, London, UK
| | - P MacPherson
- Malawi Liverpool Wellcome Trust Clinical Research Programme, Blantyre, University of Glasgow, Glasgow, Scotland, UK
| | - E L Corbett
- London School of Hygiene & Tropical Medicine, London, UK
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McIntyre-Nolan S, Kumar V, Mark-Carew M, Kumar K, Nightingale ES, Dalla Libera Marchiori G, Rogers ME, Kristan M, Campino S, Medley GF, Das P, Cameron MM. Comparison of collection methods for Phlebotomus argentipes sand flies to use in a molecular xenomonitoring system for the surveillance of visceral leishmaniasis. PLoS Negl Trop Dis 2023; 17:e0011200. [PMID: 37656745 PMCID: PMC10501600 DOI: 10.1371/journal.pntd.0011200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 09/14/2023] [Accepted: 08/14/2023] [Indexed: 09/03/2023] Open
Abstract
BACKGROUND The kala-azar elimination programme has resulted in a significant reduction in visceral leishmaniasis (VL) cases across the Indian Subcontinent. To detect any resurgence of transmission, a sensitive cost-effective surveillance system is required. Molecular xenomonitoring (MX), detection of pathogen DNA/RNA in vectors, provides a proxy of human infection in the lymphatic filariasis elimination programme. To determine whether MX can be used for VL surveillance in a low transmission setting, large numbers of the sand fly vector Phlebotomus argentipes are required. This study will determine the best method for capturing P. argentipes females for MX. METHODOLOGY/PRINCIPAL FINDINGS The field study was performed in two programmatic and two non-programmatic villages in Bihar, India. A total of 48 households (12/village) were recruited. Centers for Disease Control and Prevention light traps (CDC-LTs) were compared with Improved Prokopack (PKP) and mechanical vacuum aspirators (MVA) using standardised methods. Four 12x12 Latin squares, 576 collections, were attempted (12/house, 144/village,192/method). Molecular analyses of collections were conducted to confirm identification of P. argentipes and to detect human and Leishmania DNA. Operational factors, such as time burden, acceptance to householders and RNA preservation, were also considered. A total of 562 collections (97.7%) were completed with 6,809 sand flies captured. Females comprised 49.0% of captures, of which 1,934 (57.9%) were identified as P. argentipes. CDC-LTs collected 4.04 times more P. argentipes females than MVA and 3.62 times more than PKP (p<0.0001 for each). Of 21,735 mosquitoes in the same collections, no significant differences between collection methods were observed. CDC-LTs took less time to install and collect than to perform aspirations and their greater yield compensated for increased sorting time. No significant differences in Leishmania RNA detection and quantitation between methods were observed in experimentally infected sand flies maintained in conditions simulating field conditions. CDC-LTs were favoured by householders. CONCLUSIONS/SIGNIFICANCE CDC-LTs are the most useful collection tool of those tested for MX surveillance since they collected higher numbers of P. argentipes females without compromising mosquito captures or the preservation of RNA. However, capture rates are still low.
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Affiliation(s)
- Shannon McIntyre-Nolan
- Department of Disease Control, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Vijay Kumar
- Rajendra Memorial Research Institute of Medical Sciences, Patna, India
| | - Miguella Mark-Carew
- Department of Disease Control, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Kundan Kumar
- Rajendra Memorial Research Institute of Medical Sciences, Patna, India
| | - Emily S. Nightingale
- Department of Global Health and Development, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | | | - Matthew E. Rogers
- Department of Disease Control, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Mojca Kristan
- Department of Disease Control, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Susana Campino
- Department of Infection Biology, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Graham F. Medley
- Department of Global Health and Development, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Pradeep Das
- Department of Molecular Parasitology, National Institute of Cholera and Enteric Diseases, Kolkata, India
| | - Mary M. Cameron
- Department of Disease Control, London School of Hygiene & Tropical Medicine, London, United Kingdom
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Nightingale ES, Abbott S, Russell TW, Lowe R, Medley GF, Brady OJ. Correction: The local burden of disease during the first wave of the COVID-19 epidemic in England: estimation using different data sources from changing surveillance practices. BMC Public Health 2022; 22:1140. [PMID: 35672722 PMCID: PMC9172085 DOI: 10.1186/s12889-022-13320-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Emily S. Nightingale
- grid.8991.90000 0004 0425 469XDepartment of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK ,grid.8991.90000 0004 0425 469XCentre for Mathematical Modelling of Infectious Disease (CMMID), London School of Hygiene & Tropical Medicine, London, UK
| | - Sam Abbott
- grid.8991.90000 0004 0425 469XCentre for Mathematical Modelling of Infectious Disease (CMMID), London School of Hygiene & Tropical Medicine, London, UK ,grid.8991.90000 0004 0425 469XDepartment of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Timothy W. Russell
- grid.8991.90000 0004 0425 469XCentre for Mathematical Modelling of Infectious Disease (CMMID), London School of Hygiene & Tropical Medicine, London, UK ,grid.8991.90000 0004 0425 469XDepartment of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | | | - Rachel Lowe
- grid.8991.90000 0004 0425 469XCentre for Mathematical Modelling of Infectious Disease (CMMID), London School of Hygiene & Tropical Medicine, London, UK ,grid.8991.90000 0004 0425 469XCentre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK ,grid.10097.3f0000 0004 0387 1602Barcelona Supercomputing Centre (BSC), Barcelona, Spain ,grid.425902.80000 0000 9601 989XCatalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
| | - Graham F. Medley
- grid.8991.90000 0004 0425 469XDepartment of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK ,grid.8991.90000 0004 0425 469XCentre for Mathematical Modelling of Infectious Disease (CMMID), London School of Hygiene & Tropical Medicine, London, UK
| | - Oliver J. Brady
- grid.8991.90000 0004 0425 469XCentre for Mathematical Modelling of Infectious Disease (CMMID), London School of Hygiene & Tropical Medicine, London, UK ,grid.8991.90000 0004 0425 469XDepartment of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
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Nightingale ES, Abbott S, Russell TW, Lowe R, Medley GF, Brady OJ. The local burden of disease during the first wave of the COVID-19 epidemic in England: estimation using different data sources from changing surveillance practices. BMC Public Health 2022; 22:716. [PMID: 35410184 PMCID: PMC8996221 DOI: 10.1186/s12889-022-13069-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 03/14/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND The COVID-19 epidemic has differentially impacted communities across England, with regional variation in rates of confirmed cases, hospitalisations and deaths. Measurement of this burden changed substantially over the first months, as surveillance was expanded to accommodate the escalating epidemic. Laboratory confirmation was initially restricted to clinical need ("pillar 1") before expanding to community-wide symptomatics ("pillar 2"). This study aimed to ascertain whether inconsistent measurement of case data resulting from varying testing coverage could be reconciled by drawing inference from COVID-19-related deaths. METHODS We fit a Bayesian spatio-temporal model to weekly COVID-19-related deaths per local authority (LTLA) throughout the first wave (1 January 2020-30 June 2020), adjusting for the local epidemic timing and the age, deprivation and ethnic composition of its population. We combined predictions from this model with case data under community-wide, symptomatic testing and infection prevalence estimates from the ONS infection survey, to infer the likely trajectory of infections implied by the deaths in each LTLA. RESULTS A model including temporally- and spatially-correlated random effects was found to best accommodate the observed variation in COVID-19-related deaths, after accounting for local population characteristics. Predicted case counts under community-wide symptomatic testing suggest a total of 275,000-420,000 cases over the first wave - a median of over 100,000 additional to the total confirmed in practice under varying testing coverage. This translates to a peak incidence of around 200,000 total infections per week across England. The extent to which estimated total infections are reflected in confirmed case counts was found to vary substantially across LTLAs, ranging from 7% in Leicester to 96% in Gloucester with a median of 23%. CONCLUSIONS Limitations in testing capacity biased the observed trajectory of COVID-19 infections throughout the first wave. Basing inference on COVID-19-related mortality and higher-coverage testing later in the time period, we could explore the extent of this bias more explicitly. Evidence points towards substantial under-representation of initial growth and peak magnitude of infections nationally, to which different parts of the country contribute unequally.
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Affiliation(s)
- Emily S Nightingale
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK.
- Centre for Mathematical Modelling of Infectious Disease (CMMID), London School of Hygiene & Tropical Medicine, London, UK.
| | - Sam Abbott
- Centre for Mathematical Modelling of Infectious Disease (CMMID), London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Timothy W Russell
- Centre for Mathematical Modelling of Infectious Disease (CMMID), London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Rachel Lowe
- Centre for Mathematical Modelling of Infectious Disease (CMMID), London School of Hygiene & Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
- Barcelona Supercomputing Centre (BSC), Barcelona, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
| | - Graham F Medley
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK
- Centre for Mathematical Modelling of Infectious Disease (CMMID), London School of Hygiene & Tropical Medicine, London, UK
| | - Oliver J Brady
- Centre for Mathematical Modelling of Infectious Disease (CMMID), London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
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Nightingale ES, Brady OJ, Yakob L. The importance of saturating density dependence for population-level predictions of SARS-CoV-2 resurgence compared with density-independent or linearly density-dependent models, England, 23 March to 31 July 2020. Euro Surveill 2021; 26:2001809. [PMID: 34886944 PMCID: PMC8662798 DOI: 10.2807/1560-7917.es.2021.26.49.2001809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 04/13/2021] [Indexed: 11/20/2022] Open
Abstract
BackgroundPopulation-level mathematical models of outbreaks typically assume that disease transmission is not impacted by population density ('frequency-dependent') or that it increases linearly with density ('density-dependent').AimWe sought evidence for the role of population density in SARS-CoV-2 transmission.MethodsUsing COVID-19-associated mortality data from England, we fitted multiple functional forms linking density with transmission. We projected forwards beyond lockdown to ascertain the consequences of different functional forms on infection resurgence.ResultsCOVID-19-associated mortality data from England show evidence of increasing with population density until a saturating level, after adjusting for local age distribution, deprivation, proportion of ethnic minority population and proportion of key workers among the working population. Projections from a mathematical model that accounts for this observation deviate markedly from the current status quo for SARS-CoV-2 models which either assume linearity between density and transmission (30% of models) or no relationship at all (70%). Respectively, these classical model structures over- and underestimate the delay in infection resurgence following the release of lockdown.ConclusionIdentifying saturation points for given populations and including transmission terms that account for this feature will improve model accuracy and utility for the current and future pandemics.
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Affiliation(s)
- Emily S Nightingale
- Department of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Centre of Mathematical Modelling for Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Oliver J Brady
- Department of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Centre of Mathematical Modelling for Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Laith Yakob
- Centre of Mathematical Modelling for Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Department of Disease Control, Faculty of Infectious & Tropical Medicine, London School of Hygiene & Tropical Medicine, London, United Kingdom
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Leclerc QJ, Nightingale ES, Abbott S, Jombart T. Analysis of temporal trends in potential COVID-19 cases reported through NHS Pathways England. Sci Rep 2021; 11:7106. [PMID: 33782427 PMCID: PMC8007605 DOI: 10.1038/s41598-021-86266-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 12/16/2020] [Indexed: 01/28/2023] Open
Abstract
The National Health Service (NHS) Pathways triage system collates data on enquiries to 111 and 999 services in England. Since the 18th of March 2020, these data have been made publically available for potential COVID-19 symptoms self-reported by members of the public. Trends in such reports over time are likely to reflect behaviour of the ongoing epidemic within the wider community, potentially capturing valuable information across a broader severity profile of cases than hospital admission data. We present a fully reproducible analysis of temporal trends in NHS Pathways reports until 14th May 2020, nationally and regionally, and demonstrate that rates of growth/decline and effective reproduction number estimated from these data may be useful in monitoring transmission. This is a particularly pressing issue as lockdown restrictions begin to be lifted and evidence of disease resurgence must be constantly reassessed. We further assess the correlation between NHS Pathways reports and a publicly available NHS dataset of COVID-19-associated deaths in England, finding that enquiries to 111/999 were strongly associated with daily deaths reported 16 days later. Our results highlight the potential of NHS Pathways as the basis of an early warning system. However, this dataset relies on self-reported symptoms, which are at risk of being severely biased. Further detailed work is therefore necessary to investigate potential behavioural issues which might otherwise explain our conclusions.
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Affiliation(s)
- Quentin J Leclerc
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Emily S Nightingale
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK.
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK.
| | - Sam Abbott
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Thibaut Jombart
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
- UK Public Health Rapid Support Team, London, UK
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College, London, UK
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Rees EM, Nightingale ES, Jafari Y, Waterlow NR, Clifford S, B Pearson CA, Group CW, Jombart T, Procter SR, Knight GM. COVID-19 length of hospital stay: a systematic review and data synthesis. BMC Med 2020; 18:270. [PMID: 32878619 DOI: 10.1101/2020.04.30.20084780v3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 07/30/2020] [Indexed: 05/23/2023] Open
Abstract
BACKGROUND The COVID-19 pandemic has placed an unprecedented strain on health systems, with rapidly increasing demand for healthcare in hospitals and intensive care units (ICUs) worldwide. As the pandemic escalates, determining the resulting needs for healthcare resources (beds, staff, equipment) has become a key priority for many countries. Projecting future demand requires estimates of how long patients with COVID-19 need different levels of hospital care. METHODS We performed a systematic review of early evidence on length of stay (LoS) of patients with COVID-19 in hospital and in ICU. We subsequently developed a method to generate LoS distributions which combines summary statistics reported in multiple studies, accounting for differences in sample sizes. Applying this approach, we provide distributions for total hospital and ICU LoS from studies in China and elsewhere, for use by the community. RESULTS We identified 52 studies, the majority from China (46/52). Median hospital LoS ranged from 4 to 53 days within China, and 4 to 21 days outside of China, across 45 studies. ICU LoS was reported by eight studies-four each within and outside China-with median values ranging from 6 to 12 and 4 to 19 days, respectively. Our summary distributions have a median hospital LoS of 14 (IQR 10-19) days for China, compared with 5 (IQR 3-9) days outside of China. For ICU, the summary distributions are more similar (median (IQR) of 8 (5-13) days for China and 7 (4-11) days outside of China). There was a visible difference by discharge status, with patients who were discharged alive having longer LoS than those who died during their admission, but no trend associated with study date. CONCLUSION Patients with COVID-19 in China appeared to remain in hospital for longer than elsewhere. This may be explained by differences in criteria for admission and discharge between countries, and different timing within the pandemic. In the absence of local data, the combined summary LoS distributions provided here can be used to model bed demands for contingency planning and then updated, with the novel method presented here, as more studies with aggregated statistics emerge outside China.
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Affiliation(s)
- Eleanor M Rees
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK.
| | - Emily S Nightingale
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Yalda Jafari
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Naomi R Waterlow
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Samuel Clifford
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Carl A B Pearson
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, Republic of South Africa
| | - Cmmid Working Group
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Thibaut Jombart
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
- UK Public Health Rapid Support Team, London, UK
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College, London, UK
| | - Simon R Procter
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Gwenan M Knight
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
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Rees EM, Nightingale ES, Jafari Y, Waterlow NR, Clifford S, B Pearson CA, Group CW, Jombart T, Procter SR, Knight GM. COVID-19 length of hospital stay: a systematic review and data synthesis. BMC Med 2020; 18:270. [PMID: 32878619 DOI: 10.1101/2020.04.30.20084780] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 07/30/2020] [Indexed: 05/21/2023] Open
Abstract
BACKGROUND The COVID-19 pandemic has placed an unprecedented strain on health systems, with rapidly increasing demand for healthcare in hospitals and intensive care units (ICUs) worldwide. As the pandemic escalates, determining the resulting needs for healthcare resources (beds, staff, equipment) has become a key priority for many countries. Projecting future demand requires estimates of how long patients with COVID-19 need different levels of hospital care. METHODS We performed a systematic review of early evidence on length of stay (LoS) of patients with COVID-19 in hospital and in ICU. We subsequently developed a method to generate LoS distributions which combines summary statistics reported in multiple studies, accounting for differences in sample sizes. Applying this approach, we provide distributions for total hospital and ICU LoS from studies in China and elsewhere, for use by the community. RESULTS We identified 52 studies, the majority from China (46/52). Median hospital LoS ranged from 4 to 53 days within China, and 4 to 21 days outside of China, across 45 studies. ICU LoS was reported by eight studies-four each within and outside China-with median values ranging from 6 to 12 and 4 to 19 days, respectively. Our summary distributions have a median hospital LoS of 14 (IQR 10-19) days for China, compared with 5 (IQR 3-9) days outside of China. For ICU, the summary distributions are more similar (median (IQR) of 8 (5-13) days for China and 7 (4-11) days outside of China). There was a visible difference by discharge status, with patients who were discharged alive having longer LoS than those who died during their admission, but no trend associated with study date. CONCLUSION Patients with COVID-19 in China appeared to remain in hospital for longer than elsewhere. This may be explained by differences in criteria for admission and discharge between countries, and different timing within the pandemic. In the absence of local data, the combined summary LoS distributions provided here can be used to model bed demands for contingency planning and then updated, with the novel method presented here, as more studies with aggregated statistics emerge outside China.
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Affiliation(s)
- Eleanor M Rees
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK.
| | - Emily S Nightingale
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Yalda Jafari
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Naomi R Waterlow
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Samuel Clifford
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Carl A B Pearson
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, Republic of South Africa
| | - Cmmid Working Group
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Thibaut Jombart
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
- UK Public Health Rapid Support Team, London, UK
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College, London, UK
| | - Simon R Procter
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Gwenan M Knight
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
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Rees EM, Nightingale ES, Jafari Y, Waterlow NR, Clifford S, B Pearson CA, Group CW, Jombart T, Procter SR, Knight GM. COVID-19 length of hospital stay: a systematic review and data synthesis. BMC Med 2020; 18:270. [PMID: 32878619 PMCID: PMC7467845 DOI: 10.1186/s12916-020-01726-3] [Citation(s) in RCA: 303] [Impact Index Per Article: 75.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 07/30/2020] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND The COVID-19 pandemic has placed an unprecedented strain on health systems, with rapidly increasing demand for healthcare in hospitals and intensive care units (ICUs) worldwide. As the pandemic escalates, determining the resulting needs for healthcare resources (beds, staff, equipment) has become a key priority for many countries. Projecting future demand requires estimates of how long patients with COVID-19 need different levels of hospital care. METHODS We performed a systematic review of early evidence on length of stay (LoS) of patients with COVID-19 in hospital and in ICU. We subsequently developed a method to generate LoS distributions which combines summary statistics reported in multiple studies, accounting for differences in sample sizes. Applying this approach, we provide distributions for total hospital and ICU LoS from studies in China and elsewhere, for use by the community. RESULTS We identified 52 studies, the majority from China (46/52). Median hospital LoS ranged from 4 to 53 days within China, and 4 to 21 days outside of China, across 45 studies. ICU LoS was reported by eight studies-four each within and outside China-with median values ranging from 6 to 12 and 4 to 19 days, respectively. Our summary distributions have a median hospital LoS of 14 (IQR 10-19) days for China, compared with 5 (IQR 3-9) days outside of China. For ICU, the summary distributions are more similar (median (IQR) of 8 (5-13) days for China and 7 (4-11) days outside of China). There was a visible difference by discharge status, with patients who were discharged alive having longer LoS than those who died during their admission, but no trend associated with study date. CONCLUSION Patients with COVID-19 in China appeared to remain in hospital for longer than elsewhere. This may be explained by differences in criteria for admission and discharge between countries, and different timing within the pandemic. In the absence of local data, the combined summary LoS distributions provided here can be used to model bed demands for contingency planning and then updated, with the novel method presented here, as more studies with aggregated statistics emerge outside China.
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Affiliation(s)
- Eleanor M Rees
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK.
| | - Emily S Nightingale
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Yalda Jafari
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Naomi R Waterlow
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Samuel Clifford
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Carl A B Pearson
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, Republic of South Africa
| | - Cmmid Working Group
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Thibaut Jombart
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
- UK Public Health Rapid Support Team, London, UK
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College, London, UK
| | - Simon R Procter
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Gwenan M Knight
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
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Baguelin M, Medley GF, Nightingale ES, O’Reilly KM, Rees EM, Waterlow NR, Wagner M. Tooling-up for infectious disease transmission modelling. Epidemics 2020; 32:100395. [PMID: 32405321 PMCID: PMC7219405 DOI: 10.1016/j.epidem.2020.100395] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 05/09/2020] [Indexed: 12/15/2022] Open
Abstract
In this introduction to the Special Issue on methods for modelling of infectious disease epidemiology we provide a commentary and overview of the field. We suggest that the field has been through three revolutions that have focussed on specific methodological developments; disease dynamics and heterogeneity, advanced computing and inference, and complexity and application to the real-world. Infectious disease dynamics and heterogeneity dominated until the 1980s where the use of analytical models illustrated fundamental concepts such as herd immunity. The second revolution embraced the integration of data with models and the increased use of computing. From the turn of the century an emergence of novel datasets enabled improved modelling of real-world complexity. The emergence of more complex data that reflect the real-world heterogeneities in transmission resulted in the development of improved inference methods such as particle filtering. Each of these three revolutions have always kept the understanding of infectious disease spread as its motivation but have been developed through the use of new techniques, tools and the availability of data. We conclude by providing a commentary on what the next revoluition in infectious disease modelling may be.
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Affiliation(s)
- Marc Baguelin
- School of Public Health, Infectious Disease Epidemiology, Imperial College London, United Kingdom
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Graham F. Medley
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Emily S. Nightingale
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Kathleen M. O’Reilly
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Eleanor M. Rees
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Naomi R. Waterlow
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Moritz Wagner
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
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Jit M, Jombart T, Nightingale ES, Endo A, Abbott S, Edmunds WJ. Estimating number of cases and spread of coronavirus disease (COVID-19) using critical care admissions, United Kingdom, February to March 2020. Euro Surveill 2020; 25:2000632. [PMID: 32400358 PMCID: PMC7219029 DOI: 10.2807/1560-7917.es.2020.25.18.2000632] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 05/05/2020] [Indexed: 12/26/2022] Open
Abstract
An exponential growth model was fitted to critical care admissions from two surveillance databases to determine likely coronavirus disease (COVID-19) case numbers, critical care admissions and epidemic growth in the United Kingdom before the national lockdown. We estimate, on 23 March, a median of 114,000 (95% credible interval (CrI): 78,000-173,000) new cases and 258 (95% CrI: 220-319) new critical care reports, with 527,000 (95% CrI: 362,000-797,000) cumulative cases since 16 February.
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Affiliation(s)
- Mark Jit
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
- School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China
| | - Thibaut Jombart
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
- United Kingdom Public Health Rapid Support Team, London, United Kingdom
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, United Kingdom
| | - Emily S Nightingale
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Akira Endo
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Sam Abbott
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - W John Edmunds
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
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