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  1. Article ; Online: Optimality of Maximal-Effort Vaccination.

    Penn, Matthew J / Donnelly, Christl A

    Bulletin of mathematical biology

    2023  Volume 85, Issue 8, Page(s) 73

    Abstract: It is widely acknowledged that vaccinating at maximal effort in the face of an ongoing epidemic is the best strategy to minimise infections and deaths from the disease. Despite this, no one has proved that this is guaranteed to be true if the disease ... ...

    Abstract It is widely acknowledged that vaccinating at maximal effort in the face of an ongoing epidemic is the best strategy to minimise infections and deaths from the disease. Despite this, no one has proved that this is guaranteed to be true if the disease follows multi-group SIR (Susceptible-Infected-Recovered) dynamics. This paper provides a novel proof of this principle for the existing SIR framework, showing that the total number of deaths or infections from an epidemic is decreasing in vaccination effort. Furthermore, it presents a novel model for vaccination which assumes that vaccines assigned to a subgroup are distributed randomly to the unvaccinated population of that subgroup. It suggests, using COVID-19 data, that this more accurately captures vaccination dynamics than the model commonly found in the literature. However, as the novel model provides a strictly larger set of possible vaccination policies, the results presented in this paper hold for both models.
    MeSH term(s) Humans ; Models, Biological ; Mathematical Concepts ; COVID-19/epidemiology ; COVID-19/prevention & control ; Epidemics/prevention & control ; Vaccination/methods
    Language English
    Publishing date 2023-06-23
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 184905-0
    ISSN 1522-9602 ; 0007-4985 ; 0092-8240
    ISSN (online) 1522-9602
    ISSN 0007-4985 ; 0092-8240
    DOI 10.1007/s11538-023-01179-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Asymptotic Analysis of Optimal Vaccination Policies.

    Penn, Matthew J / Donnelly, Christl A

    Bulletin of mathematical biology

    2023  Volume 85, Issue 3, Page(s) 15

    Abstract: Targeted vaccination policies can have a significant impact on the number of infections and deaths in an epidemic. However, optimising such policies is complicated, and the resultant solution may be difficult to explain to policy-makers and to the public. ...

    Abstract Targeted vaccination policies can have a significant impact on the number of infections and deaths in an epidemic. However, optimising such policies is complicated, and the resultant solution may be difficult to explain to policy-makers and to the public. The key novelty of this paper is a derivation of the leading-order optimal vaccination policy under multi-group susceptible-infected-recovered dynamics in two different cases. Firstly, it considers the case of a small vulnerable subgroup in a population and shows that (in the asymptotic limit) it is optimal to vaccinate this group first, regardless of the properties of the other groups. Then, it considers the case of a small vaccine supply and transforms the optimal vaccination problem into a simple knapsack problem by linearising the final size equations. Both of these cases are then explored further through numerical examples, which show that these solutions are also directly useful for realistic parameter values. Moreover, the findings of this paper give some general principles for optimal vaccination policies which will help policy-makers and the public to understand the reasoning behind optimal vaccination programs in more generic cases.
    MeSH term(s) Models, Biological ; Mathematical Concepts ; Vaccination ; Epidemics/prevention & control ; Policy
    Language English
    Publishing date 2023-01-20
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 184905-0
    ISSN 1522-9602 ; 0007-4985 ; 0092-8240
    ISSN (online) 1522-9602
    ISSN 0007-4985 ; 0092-8240
    DOI 10.1007/s11538-022-01114-3
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  3. Article ; Online: Fundamental limits on inferring epidemic resurgence in real time using effective reproduction numbers.

    Parag, Kris V / Donnelly, Christl A

    PLoS computational biology

    2022  Volume 18, Issue 4, Page(s) e1010004

    Abstract: We find that epidemic resurgence, defined as an upswing in the effective reproduction number (R) of the contagion from subcritical to supercritical values, is fundamentally difficult to detect in real time. Inherent latencies in pathogen transmission, ... ...

    Abstract We find that epidemic resurgence, defined as an upswing in the effective reproduction number (R) of the contagion from subcritical to supercritical values, is fundamentally difficult to detect in real time. Inherent latencies in pathogen transmission, coupled with smaller and intrinsically noisier case incidence across periods of subcritical spread, mean that resurgence cannot be reliably detected without significant delays of the order of the generation time of the disease, even when case reporting is perfect. In contrast, epidemic suppression (where R falls from supercritical to subcritical values) may be ascertained 5-10 times faster due to the naturally larger incidence at which control actions are generally applied. We prove that these innate limits on detecting resurgence only worsen when spatial or demographic heterogeneities are incorporated. Consequently, we argue that resurgence is more effectively handled proactively, potentially at the expense of false alarms. Timely responses to recrudescent infections or emerging variants of concern are more likely to be possible when policy is informed by a greater quality and diversity of surveillance data than by further optimisation of the statistical models used to process routine outbreak data.
    MeSH term(s) Basic Reproduction Number ; Disease Outbreaks ; Epidemics ; Incidence
    Language English
    Publishing date 2022-04-11
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2193340-6
    ISSN 1553-7358 ; 1553-734X
    ISSN (online) 1553-7358
    ISSN 1553-734X
    DOI 10.1371/journal.pcbi.1010004
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  4. Article ; Online: The impact of repeated rapid test strategies on the effectiveness of at-home antiviral treatments for SARS-CoV-2.

    Menkir, Tigist F / Donnelly, Christl A

    Nature communications

    2022  Volume 13, Issue 1, Page(s) 5283

    Abstract: Regular rapid testing can provide twofold benefilts: identifying infectious individuals and providing positive tests sufficiently early during infection that treatment with antivirals can effectively inhibit development of severe disease. Here, we ... ...

    Abstract Regular rapid testing can provide twofold benefilts: identifying infectious individuals and providing positive tests sufficiently early during infection that treatment with antivirals can effectively inhibit development of severe disease. Here, we provide a quantitative illustration of the extent of nirmatrelvir-associated treatment benefits that are accrued among high-risk populations when rapid tests are administered at various intervals. Strategies for which tests are administered more frequently are associated with greater reductions in the risk of hospitalization, with weighted risk ratios for testing every other day to once every 2 weeks ranging from 0.17 (95% CI: 0.11-0.28) to 0.77 (95% CI: 0.69-0.83) and correspondingly, higher proportions of the infected population benefiting from treatment, ranging from 0.26 (95% CI: 0.18-0.34) to 0.92 (95% CI: 0.80-0.98), respectively. Importantly, reduced treatment delays, coupled with increased test and treatment coverage, have a critical influence on average treatment benefits, confirming the significance of access.
    MeSH term(s) Antiviral Agents/pharmacology ; Antiviral Agents/therapeutic use ; Hospitalization ; Humans ; Risk Factors ; SARS-CoV-2 ; COVID-19 Drug Treatment
    Chemical Substances Antiviral Agents
    Language English
    Publishing date 2022-09-08
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-022-32640-2
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  5. Article ; Online: Analysis of a double Poisson model for predicting football results in Euro 2020.

    Penn, Matthew J / Donnelly, Christl A

    PloS one

    2022  Volume 17, Issue 5, Page(s) e0268511

    Abstract: First developed in 1982, the double Poisson model, where goals scored by each team are assumed to be Poisson distributed with a mean depending on attacking and defensive strengths, remains a popular choice for predicting football scores, despite the ... ...

    Abstract First developed in 1982, the double Poisson model, where goals scored by each team are assumed to be Poisson distributed with a mean depending on attacking and defensive strengths, remains a popular choice for predicting football scores, despite the multitude of newer methods that have been developed. This paper examines the pre-tournament predictions made using this model for the Euro 2020 football tournament. These predictions won the Royal Statistical Society's prediction competition, demonstrating that even this simple model can produce high-quality results. Moreover, the paper also presents a range of novel analytic results which exactly quantify the conditions for the existence and uniqueness of the solution to the equations for the model parameters. After deriving these results, it provides a novel examination of a potential problem with the model-the over-weighting of the results of weaker teams-and illustrates the effectiveness of ignoring results against the weakest opposition. It also compares the predictions with the actual results of Euro 2020, showing that they were extremely accurate in predicting the number of goals scored. Finally, it considers the choice of start date for the dataset, and illustrates that the choice made by the authors (which was to start the dataset just after the previous major international tournament) was close to optimal, at least in this case. The findings of this study give a better understanding of the mathematical behaviour of the double Poisson model and provide evidence for its effectiveness as a match prediction tool.
    MeSH term(s) Athletic Performance ; Football ; Soccer
    Language English
    Publishing date 2022-05-19
    Publishing country United States
    Document type 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.0268511
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  6. Article ; Online: Drivers of SARS-CoV-2 testing behaviour: a modelling study using nationwide testing data in England.

    Kim, Younjung / Donnelly, Christl A / Nouvellet, Pierre

    Nature communications

    2023  Volume 14, Issue 1, Page(s) 2148

    Abstract: 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 ... ...

    Abstract 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.
    MeSH term(s) Humans ; SARS-CoV-2 ; COVID-19/diagnosis ; COVID-19/epidemiology ; COVID-19 Testing ; Pandemics/prevention & control ; Disease Susceptibility ; Communicable Disease Control ; England/epidemiology
    Language English
    Publishing date 2023-04-14
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-023-37813-1
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  7. Article ; Online: Public awareness of and opinions on the use of mathematical transmission modelling to inform public health policy in the United Kingdom.

    McCabe, Ruth / Donnelly, Christl A

    Journal of the Royal Society, Interface

    2023  Volume 20, Issue 209, Page(s) 20230456

    Abstract: Mathematical modelling is used to inform public health policy, particularly so during the COVID-19 pandemic. As the public are key stakeholders, understanding the public perceptions of these tools is vital. To complement our previous study on the science- ...

    Abstract Mathematical modelling is used to inform public health policy, particularly so during the COVID-19 pandemic. As the public are key stakeholders, understanding the public perceptions of these tools is vital. To complement our previous study on the science-policy interface, novel survey data were collected via an online panel ('representative' sample) and social media ('non-probability' sample). Many questions were asked twice, in reference to the period 'prior to' (retrospectively) and 'during' the COVID-19 pandemic. Respondents reported being increasingly aware of modelling in informing policy during the pandemic, with higher levels of awareness among social media respondents. Modelling informing policy was perceived as more reliable during the pandemic than in reference to the pre-pandemic period in both samples. Trust in government public health advice remained high within both samples but was lower during the pandemic in comparison with the (retrospective) pre-pandemic period. The decay in trust was greater among social media respondents. Many respondents explicitly made the distinction that their trust was reserved for 'scientists' and not 'politicians'. Almost all respondents believed governments have responsibility for communicating modelling to the public. These results provide a reminder of the skewed conclusions that could be drawn from non-representative samples.
    MeSH term(s) Humans ; SARS-CoV-2 ; Retrospective Studies ; Pandemics ; COVID-19/epidemiology ; Public Health ; Health Policy ; United Kingdom
    Language English
    Publishing date 2023-12-20
    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.2023.0456
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  8. Article ; Online: Inferring community transmission of SARS-CoV-2 in the United Kingdom using the ONS COVID-19 Infection Survey.

    McCabe, Ruth / Danelian, Gabriel / Panovska-Griffiths, Jasmina / Donnelly, Christl A

    Infectious Disease Modelling

    2024  Volume 9, Issue 2, Page(s) 299–313

    Abstract: Key epidemiological parameters, including the effective reproduction number, ...

    Abstract Key epidemiological parameters, including the effective reproduction number,
    Language English
    Publishing date 2024-01-30
    Publishing country China
    Document type Journal Article
    ZDB-ID 3015225-2
    ISSN 2468-0427 ; 2468-2152
    ISSN (online) 2468-0427
    ISSN 2468-2152
    DOI 10.1016/j.idm.2024.01.011
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  9. Article ; Online: Quantifying the information in noisy epidemic curves.

    Parag, Kris V / Donnelly, Christl A / Zarebski, Alexander E

    Nature computational science

    2022  Volume 2, Issue 9, Page(s) 584–594

    Abstract: Reliably estimating the dynamics of transmissible diseases from noisy surveillance data is an enduring problem in modern epidemiology. Key parameters are often inferred from incident time series, with the aim of informing policy-makers on the growth rate ...

    Abstract Reliably estimating the dynamics of transmissible diseases from noisy surveillance data is an enduring problem in modern epidemiology. Key parameters are often inferred from incident time series, with the aim of informing policy-makers on the growth rate of outbreaks or testing hypotheses about the effectiveness of public health interventions. However, the reliability of these inferences depends critically on reporting errors and latencies innate to the time series. Here, we develop an analytical framework to quantify the uncertainty induced by under-reporting and delays in reporting infections, as well as a metric for ranking surveillance data informativeness. We apply this metric to two primary data sources for inferring the instantaneous reproduction number: epidemic case and death curves. We find that the assumption of death curves as more reliable, commonly made for acute infectious diseases such as COVID-19 and influenza, is not obvious and possibly untrue in many settings. Our framework clarifies and quantifies how actionable information about pathogen transmissibility is lost due to surveillance limitations.
    MeSH term(s) Humans ; Reproducibility of Results ; COVID-19/epidemiology ; Epidemics ; Disease Outbreaks ; Communicable Diseases
    Language English
    Publishing date 2022-09-26
    Publishing country United States
    Document type Journal Article
    ISSN 2662-8457
    ISSN (online) 2662-8457
    DOI 10.1038/s43588-022-00313-1
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  10. Article: Are epidemic growth rates more informative than reproduction numbers?

    Parag, Kris V / Thompson, Robin N / Donnelly, Christl A

    Journal of the Royal Statistical Society. Series A, (Statistics in Society)

    2022  

    Abstract: statistics, often derived from simplified models of epidemic spread, inform public health policy in real time. The instantaneous reproduction number, ...

    Abstract statistics, often derived from simplified models of epidemic spread, inform public health policy in real time. The instantaneous reproduction number,
    Language English
    Publishing date 2022-05-26
    Publishing country England
    Document type Journal Article
    ZDB-ID 1490715-X
    ISSN 1467-985X ; 0964-1998 ; 0035-9238
    ISSN (online) 1467-985X
    ISSN 0964-1998 ; 0035-9238
    DOI 10.1111/rssa.12867
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