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Article ; Online: Refining Reproduction Number Estimates to Account for Unobserved Generations of Infection in Emerging Epidemics.

Brizzi, Andrea / O'Driscoll, Megan / Dorigatti, Ilaria

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America

2022 Volume 75, Issue 1, Page(s) e114–e121

Abstract: Background: Estimating the transmissibility of infectious diseases is key to inform situational awareness and for response planning. Several methods tend to overestimate the basic (R0) and effective (Rt) reproduction numbers during the initial phases of ...

Abstract	Background: Estimating the transmissibility of infectious diseases is key to inform situational awareness and for response planning. Several methods tend to overestimate the basic (R0) and effective (Rt) reproduction numbers during the initial phases of an epidemic. In this work we explore the impact of incomplete observations and underreporting of the first generations of infections during the initial epidemic phase. Methods: We propose a debiasing procedure that utilizes a linear exponential growth model to infer unobserved initial generations of infections and apply it to EpiEstim. We assess the performance of our adjustment using simulated data, considering different levels of transmissibility and reporting rates. We also apply the proposed correction to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) incidence data reported in Italy, Sweden, the United Kingdom, and the United States. Results: In all simulation scenarios, our adjustment outperforms the original EpiEstim method. The proposed correction reduces the systematic bias, and the quantification of uncertainty is more precise, as better coverage of the true R0 values is achieved with tighter credible intervals. When applied to real-world data, the proposed adjustment produces basic reproduction number estimates that closely match the estimates obtained in other studies while making use of a minimal amount of data. Conclusions: The proposed adjustment refines the reproduction number estimates obtained with the current EpiEstim implementation by producing improved, more precise estimates earlier than with the original method. This has relevant public health implications.
MeSH term(s)	Basic Reproduction Number ; COVID-19/epidemiology ; Epidemics ; Humans ; Reproduction ; SARS-CoV-2
Language	English
Publishing date	2022-02-16
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1099781-7
ISSN	1537-6591 ; 1058-4838
ISSN (online)	1537-6591
ISSN	1058-4838
DOI	10.1093/cid/ciac138
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Article ; Online: SARIMA-modelled greater severity and mortality during the 2010/11 post-pandemic influenza season compared to the 2009 H1N1 pandemic in English hospitals.

Lau, Krystal / Dorigatti, Ilaria / Miraldo, Marisa / Hauck, Katharina

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases

2021 Volume 105, Page(s) 161–171

Abstract: Objective: The COVID-19 pandemic demonstrates the need for understanding pathways to healthcare demand, morbidity, and mortality of pandemic patients. We estimate H1N1 (1) hospitalization rates, (2) severity rates (length of stay, ventilation, pneumonia, ...

Abstract	Objective: The COVID-19 pandemic demonstrates the need for understanding pathways to healthcare demand, morbidity, and mortality of pandemic patients. We estimate H1N1 (1) hospitalization rates, (2) severity rates (length of stay, ventilation, pneumonia, and death) of those hospitalized, (3) mortality rates, and (4) time lags between infections and hospitalizations during the pandemic (June 2009 to March 2010) and post-pandemic influenza season (November 2010 to February 2011) in England. Methods: Estimates of H1N1 infections from a dynamic transmission model are combined with hospitalizations and severity using time series econometric analyses of administrative patient-level hospital data. Results: Hospitalization rates were 34% higher and severity rates of those hospitalized were 20%-90% higher in the post-pandemic period than the pandemic. Adults (45-64-years-old) had the highest ventilation and pneumonia hospitalization rates. Hospitalizations did not lag infection during the pandemic for the young (<24-years-old) but lagged by one or more weeks for all ages in the post-pandemic period. Discussion: The post-pandemic flu season exhibited heightened H1N1 severity, long after the pandemic was declared over. Policymakers should remain vigilant even after pandemics seem to have subsided. Analysis of administrative hospital data and epidemiological modelling estimates can provide valuable insights to inform responses to COVID-19 and future influenza and other disease pandemics.
MeSH term(s)	Adolescent ; Adult ; Aged ; Aged, 80 and over ; Child ; Child, Preschool ; England/epidemiology ; Female ; Hospitalization/statistics & numerical data ; Humans ; Influenza A Virus, H1N1 Subtype ; Influenza, Human/epidemiology ; Influenza, Human/mortality ; Male ; Middle Aged ; Pandemics ; Severity of Illness Index ; Time Factors ; Young Adult
Language	English
Publishing date	2021-02-03
Publishing country	Canada
Document type	Comparative Study ; Journal Article
ZDB-ID	1331197-9
ISSN	1878-3511 ; 1201-9712
ISSN (online)	1878-3511
ISSN	1201-9712
DOI	10.1016/j.ijid.2021.01.070
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Article ; Online: Quantifying the risk of Zika virus spread in Asia during the 2015-16 epidemic in Latin America and the Caribbean: A modeling study.

Luo, Xue Shi / Imai, Natsuko / Dorigatti, Ilaria

Travel medicine and infectious disease

2020 Volume 33, Page(s) 101562

Abstract: Background: No large-scale Zika epidemic has been observed to date in Southeast Asia following the 2015-16 Latin American and the Caribbean epidemic. One hypothesis is Southeast Asian populations' partial immunity to Zika.: Method: We estimated the ... ...

Abstract	Background: No large-scale Zika epidemic has been observed to date in Southeast Asia following the 2015-16 Latin American and the Caribbean epidemic. One hypothesis is Southeast Asian populations' partial immunity to Zika. Method: We estimated the two conditions for a Zika outbreak emergence in Southeast Asia: (i) the risk of Zika introduction from Latin America and the Caribbean and, (ii) the risk of autochthonous transmission under varying assumptions on population immunity. We also validated the model used to estimate the risk of introduction by comparing the estimated number of Zika seeds introduced into the United States with case counts reported by the Centers for Disease Control and Prevention (CDC). Results: There was good agreement between our estimates and case counts reported by the CDC. We thus applied the model to Southeast Asia and estimated that, on average, 1-10 seeds were introduced into Indonesia, Malaysia, the Philippines, Singapore, Thailand and Vietnam. We also found increasing population immunity levels from 0 to 90% reduced probability of autochthonous transmission by 40% and increasing individual variation in transmission further reduced the outbreak probability. Conclusions: Population immunity, combined with heterogeneity in transmission, can explain why no large-scale outbreak was observed in Southeast Asia during the 2015-16 epidemic.
MeSH term(s)	Asia/epidemiology ; Caribbean Region/epidemiology ; Humans ; Latin America/epidemiology ; Risk Assessment/methods ; Risk Assessment/standards ; Risk Factors ; Zika Virus ; Zika Virus Infection/epidemiology
Language	English
Publishing date	2020-01-26
Publishing country	Netherlands
Document type	Journal Article ; Validation Study
ZDB-ID	2170891-5
ISSN	1873-0442 ; 1477-8939
ISSN (online)	1873-0442
ISSN	1477-8939
DOI	10.1016/j.tmaid.2020.101562
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Article ; Online: Comparison of machine learning methods for estimating case fatality ratios: An Ebola outbreak simulation study.

Forna, Alpha / Dorigatti, Ilaria / Nouvellet, Pierre / Donnelly, Christl A

PloS one

2021 Volume 16, Issue 9, Page(s) e0257005

Abstract: Background: Machine 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.: ... ...

Abstract	Background: Machine 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. Methods: Using 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). Results: Across 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%). Conclusion: ML 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.
MeSH term(s)	Computer Simulation ; Data Interpretation, Statistical ; Datasets as Topic ; Disease Outbreaks ; Hemorrhagic Fever, Ebola/epidemiology ; Hemorrhagic Fever, Ebola/mortality ; Humans ; Machine Learning ; Models, Statistical ; Research Design ; Survival Analysis
Language	English
Publishing date	2021-09-15
Publishing country	United States
Document type	Comparative Study ; Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0257005
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Yellow fever in Asia-a risk analysis.

Cracknell Daniels, Bethan / Gaythorpe, Katy / Imai, Natsuko / Dorigatti, Ilaria

Journal of travel medicine

2021 Volume 28, Issue 3

Abstract: Background: There is concern about the risk of yellow fever (YF) establishment in Asia, owing to rising numbers of urban outbreaks in endemic countries and globalisation. Following an outbreak in Angola in 2016, YF cases were introduced into China. ... ...

Abstract	Background: There is concern about the risk of yellow fever (YF) establishment in Asia, owing to rising numbers of urban outbreaks in endemic countries and globalisation. Following an outbreak in Angola in 2016, YF cases were introduced into China. Prior to this, YF had never been recorded in Asia, despite climatic suitability and the presence of mosquitoes. An outbreak in Asia could result in widespread fatalities and huge economic impact. Therefore, quantifying the potential risk of YF outbreaks in Asia is a public health priority. Methods: Using international flight data and YF incidence estimates from 2016, we quantified the risk of YF introduction via air travel into Asia. In locations with evidence of a competent mosquito population, the potential for autochthonous YF transmission was estimated using a temperature-dependent model of the reproduction number and a branching process model assuming a negative binomial distribution. Results: In total, 25 cities across Asia were estimated to be at risk of receiving at least one YF viraemic traveller during 2016. At their average temperatures, we estimated the probability of autochthonous transmission to be <50% in all cities, which was primarily due to the limited number of estimated introductions that year. Conclusion: Despite the rise in air travel, we found low support for travel patterns between YF endemic countries and Asia resulting in autochthonous transmission during 2016. This supports the historic absence of YF in Asia and suggests it could be due to a limited number of introductions in previous years. Future increases in travel volumes or YF incidence can increase the number of introductions and the risk of autochthonous transmission. Given the high proportion of asymptomatic or mild infections and the challenges of YF surveillance, our model can be used to estimate the introduction and outbreak risk and can provide useful information to surveillance systems.
MeSH term(s)	Aedes ; Angola/epidemiology ; Animals ; Asia ; Cities ; Disease Outbreaks/prevention & control ; Female ; Humans ; Risk Assessment ; Travel-Related Illness ; Yellow Fever/epidemiology ; Yellow Fever/prevention & control ; Yellow Fever/transmission ; Yellow fever virus/physiology
Language	English
Publishing date	2021-01-28
Publishing country	England
Document type	Journal Article
ZDB-ID	1212504-0
ISSN	1708-8305 ; 1195-1982
ISSN (online)	1708-8305
ISSN	1195-1982
DOI	10.1093/jtm/taab015
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Article ; Online: Impact of early phase COVID-19 precautionary behaviors on seasonal influenza in Hong Kong: A time-series modeling approach.

Lin, Chun-Pang / Dorigatti, Ilaria / Tsui, Kwok-Leung / Xie, Min / Ling, Man-Ho / Yuan, Hsiang-Yu

Frontiers in public health

2022 Volume 10, Page(s) 992697

Abstract: Background: Before major non-pharmaceutical interventions were implemented, seasonal incidence of influenza in Hong Kong showed a rapid and unexpected reduction immediately following the early spread of COVID-19 in mainland China in January 2020. This ... ...

Abstract	Background: Before major non-pharmaceutical interventions were implemented, seasonal incidence of influenza in Hong Kong showed a rapid and unexpected reduction immediately following the early spread of COVID-19 in mainland China in January 2020. This decline was presumably associated with precautionary behavioral changes (e.g., wearing face masks and avoiding crowded places). Knowing their effectiveness on the transmissibility of seasonal influenza can inform future influenza prevention strategies. Methods: We estimated the effective reproduction number ( Findings: The model-estimated mean Conclusion: Our model results indicate that wearing face masks and avoiding crowded places could have potentially significant suppressive impacts on influenza.
MeSH term(s)	Humans ; Influenza, Human/epidemiology ; Influenza, Human/prevention & control ; COVID-19/epidemiology ; COVID-19/prevention & control ; Bayes Theorem ; Time Factors ; Masks
Language	English
Publishing date	2022-11-14
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2711781-9
ISSN	2296-2565 ; 2296-2565
ISSN (online)	2296-2565
ISSN	2296-2565
DOI	10.3389/fpubh.2022.992697
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Article ; Online: Modelling the viral dynamics of the SARS-CoV-2 Delta and Omicron variants in different cell types.

McCormack, Clare P / Yan, Ada W C / Brown, Jonathan C / Sukhova, Ksenia / Peacock, Thomas P / Barclay, Wendy S / Dorigatti, Ilaria

Journal of the Royal Society, Interface

2023 Volume 20, Issue 205, Page(s) 20230187

Abstract: We use viral kinetic models fitted to viral load data ... ...

Abstract	We use viral kinetic models fitted to viral load data from
MeSH term(s)	Humans ; COVID-19 ; SARS-CoV-2 ; Basic Reproduction Number ; Kinetics
Language	English
Publishing date	2023-08-09
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2156283-0
ISSN	1742-5662 ; 1742-5689
ISSN (online)	1742-5662
ISSN	1742-5689
DOI	10.1098/rsif.2023.0187
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Article ; Online: Refining reproduction number estimates to account for unobserved generations of infection in emerging epidemics

Brizzi, Andrea / O'Driscoll, Megan / Dorigatti, Ilaria

medRxiv

Abstract: Background Estimating the transmissibility of infectious diseases is key to inform situational awareness and for response planning. Several methods tend to overestimate the basic (R_0) and effective (R_t) reproduction numbers during the initial phases of ...

Abstract	Background Estimating the transmissibility of infectious diseases is key to inform situational awareness and for response planning. Several methods tend to overestimate the basic (R_0) and effective (R_t) reproduction numbers during the initial phases of an epidemic. The reasons driving the observed bias are unknown. In this work we explore the impact of incomplete observations and underreporting of the first generations of infections during the initial epidemic phase. Methods We propose a debiasing procedure which utilises a linear exponential growth model to infer unobserved initial generations of infections and apply it to EpiEstim. We assess the performance of our adjustment using simulated data, considering different levels of transmissibility and reporting rates. We also apply the proposed correction to reported SARS-CoV-2 incidence data reported in Italy, Sweden, the United Kingdom and the United States of America. Results In all simulation scenarios, our adjustment outperforms the original EpiEstim method. The proposed correction reduces the systematic bias and the quantification of uncertainty is more precise, as better coverage of the true R_0 values is achieved with tighter credible intervals. When applied to real world data, the proposed adjustment produces basic reproduction number estimates which closely match the estimates obtained in other studies while making use of a minimal amount of data. Conclusions The proposed adjustment refines the reproduction number estimates obtained with the current EpiEstim implementation by producing improved, more precise estimates earlier than with the original method. This has relevant public health implications.
Keywords	covid19
Language	English
Publishing date	2021-11-09
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2021.11.08.21266033
Database	COVID19

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Article ; Online: Spatiotemporal variability in case fatality ratios for the 2013-2016 Ebola epidemic in West Africa.

Forna, Alpha / Dorigatti, Ilaria / Nouvellet, Pierre / Donnelly, Christl A

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases

2020 Volume 93, Page(s) 48–55

Abstract: Background: For the 2013-2016 Ebola epidemic in West Africa, the largest Ebola virus disease (EVD) epidemic to date, we aim to analyse the patient mix in detail to characterise key sources of spatiotemporal heterogeneity in the case fatality ratios (CFR) ...

Abstract	Background: For the 2013-2016 Ebola epidemic in West Africa, the largest Ebola virus disease (EVD) epidemic to date, we aim to analyse the patient mix in detail to characterise key sources of spatiotemporal heterogeneity in the case fatality ratios (CFR). Methods: We applied a non-parametric Boosted Regression Trees (BRT) imputation approach for patients with missing survival outcomes and adjusted for model imperfection. Semivariogram analysis and kriging were used to investigate spatiotemporal heterogeneities. Results: CFR estimates varied significantly between districts and over time over the course of the epidemic. BRT modelling accounted for most of the spatiotemporal variation and interactions in CFR, but moderate spatial autocorrelation remained for distances up to approximately 90 km. Combining district-level CFR estimates and kriged district-level residuals provided the best linear unbiased predicted map of CFR accounting for the both explained and unexplained spatial variation. Temporal autocorrelation was not observed in the district-level residuals from the BRT estimates. Conclusions: This study provides new insight into the epidemiology of the 2013-2016 West African Ebola epidemic with a view of informing future public health contingency planning, resource allocation and impact assessment. The analytical framework developed in this analysis, coupled with key domain knowledge, could be deployed in real time to support the response to ongoing and future outbreaks.
MeSH term(s)	Africa, Western/epidemiology ; Epidemics ; Hemorrhagic Fever, Ebola/epidemiology ; Hemorrhagic Fever, Ebola/mortality ; Humans ; Spatial Analysis
Language	English
Publishing date	2020-01-28
Publishing country	Canada
Document type	Journal Article
ZDB-ID	1331197-9
ISSN	1878-3511 ; 1201-9712
ISSN (online)	1878-3511
ISSN	1201-9712
DOI	10.1016/j.ijid.2020.01.046
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Article ; Online: Estimating dengue transmission intensity from serological data: A comparative analysis using mixture and catalytic models.

Cox, Victoria / O'Driscoll, Megan / Imai, Natsuko / Prayitno, Ari / Hadinegoro, Sri Rezeki / Taurel, Anne-Frieda / Coudeville, Laurent / Dorigatti, Ilaria

PLoS neglected tropical diseases

2022 Volume 16, Issue 7, Page(s) e0010592

Abstract: Background: Dengue virus (DENV) infection is a global health concern of increasing magnitude. To target intervention strategies, accurate estimates of the force of infection (FOI) are necessary. Catalytic models have been widely used to estimate DENV ... ...

Abstract	Background: Dengue virus (DENV) infection is a global health concern of increasing magnitude. To target intervention strategies, accurate estimates of the force of infection (FOI) are necessary. Catalytic models have been widely used to estimate DENV FOI and rely on a binary classification of serostatus as seropositive or seronegative, according to pre-defined antibody thresholds. Previous work has demonstrated the use of thresholds can cause serostatus misclassification and biased estimates. In contrast, mixture models do not rely on thresholds and use the full distribution of antibody titres. To date, there has been limited application of mixture models to estimate DENV FOI. Methods: We compare the application of mixture models and time-constant and time-varying catalytic models to simulated data and to serological data collected in Vietnam from 2004 to 2009 (N ≥ 2178) and Indonesia in 2014 (N = 3194). Results: The simulation study showed larger mean FOI estimate bias from the time-constant and time-varying catalytic models (-0.007 (95% Confidence Interval (CI): -0.069, 0.029) and -0.006 (95% CI -0.095, 0.043)) than from the mixture model (0.001 (95% CI -0.036, 0.065)). Coverage of the true FOI was > 95% for estimates from both the time-varying catalytic and mixture model, however the latter had reduced uncertainty. When applied to real data from Vietnam, the mixture model frequently produced higher FOI and seroprevalence estimates than the catalytic models. Conclusions: Our results suggest mixture models represent valid, potentially less biased, alternatives to catalytic models, which could be particularly useful when estimating FOI from data with largely overlapping antibody titre distributions.
MeSH term(s)	Dengue ; Humans ; Indonesia/epidemiology ; Seroepidemiologic Studies ; Vietnam/epidemiology
Language	English
Publishing date	2022-07-11
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2429704-5
ISSN	1935-2735 ; 1935-2735
ISSN (online)	1935-2735
ISSN	1935-2735
DOI	10.1371/journal.pntd.0010592
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