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  1. Article ; Online: Quantifying the direct and indirect protection provided by insecticide treated bed nets against malaria.

    Unwin, H Juliette T / Sherrard-Smith, Ellie / Churcher, Thomas S / Ghani, Azra C

    Nature communications

    2023  Volume 14, Issue 1, Page(s) 676

    Abstract: Long lasting insecticidal nets (LLINs) provide both direct and indirect protection against malaria. As pyrethroid resistance evolves in mosquito vectors, it will be useful to understand how the specific benefits LLINs afford individuals and communities ... ...

    Abstract Long lasting insecticidal nets (LLINs) provide both direct and indirect protection against malaria. As pyrethroid resistance evolves in mosquito vectors, it will be useful to understand how the specific benefits LLINs afford individuals and communities may be affected. Here we use modelling to show that there is no minimum LLIN usage needed for users and non-users to benefit from community protection. Modelling results also indicate that pyrethroid resistance in local mosquitoes will likely diminish the direct and indirect benefits from insecticides, leaving the barrier effects intact, but LLINs are still expected to provide enhanced benefit over untreated nets even at high levels of pyrethroid resistance.
    MeSH term(s) Animals ; Humans ; Pyrethrins ; Mosquito Control/methods ; Insecticide Resistance ; Insecticide-Treated Bednets ; Anopheles ; Insecticides/pharmacology ; Malaria/prevention & control
    Chemical Substances Pyrethrins ; Insecticides
    Language English
    Publishing date 2023-02-08
    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/s41467-023-36356-9
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Quantifying the direct and indirect protection provided by insecticide treated bed nets against malaria

    H. Juliette T. Unwin / Ellie Sherrard-Smith / Thomas S. Churcher / Azra C. Ghani

    Nature Communications, Vol 14, Iss 1, Pp 1-

    2023  Volume 12

    Abstract: Long lasting insecticide treated mosquito nets (LLINs) provide protection from malaria through both direct effects to the user and indirect community-level effects. Here, the authors use mathematical modelling to assess the relative contributions of ... ...

    Abstract Long lasting insecticide treated mosquito nets (LLINs) provide protection from malaria through both direct effects to the user and indirect community-level effects. Here, the authors use mathematical modelling to assess the relative contributions of these effects under different insecticide resistance and LLIN usage scenarios.
    Keywords Science ; Q
    Language English
    Publishing date 2023-02-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: List child dependents on death certificates.

    Flaxman, Seth / Kasonka, Lackson / Cluver, Lucie / Souza, Andrea Santos / Nelson, Charles A / Blenkinsop, Alexandra / Unwin, H Juliette T / Hillis, Susan

    Science (New York, N.Y.)

    2023  Volume 380, Issue 6644, Page(s) 467

    MeSH term(s) Child ; Humans ; Child, Orphaned ; COVID-19/mortality ; Death Certificates ; Models, Statistical ; Cause of Death
    Language English
    Publishing date 2023-05-04
    Publishing country United States
    Document type Letter
    ZDB-ID 128410-1
    ISSN 1095-9203 ; 0036-8075
    ISSN (online) 1095-9203
    ISSN 0036-8075
    DOI 10.1126/science.adh8784
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  4. Article ; Online: Inference of malaria reproduction numbers in three elimination settings by combining temporal data and distance metrics.

    Routledge, Isobel / Unwin, H Juliette T / Bhatt, Samir

    Scientific reports

    2021  Volume 11, Issue 1, Page(s) 14495

    Abstract: Individual-level geographic information about malaria cases, such as the GPS coordinates of residence or health facility, is often collected as part of surveillance in near-elimination settings, but could be more effectively utilised to infer ... ...

    Abstract Individual-level geographic information about malaria cases, such as the GPS coordinates of residence or health facility, is often collected as part of surveillance in near-elimination settings, but could be more effectively utilised to infer transmission dynamics, in conjunction with additional information such as symptom onset time and genetic distance. However, in the absence of data about the flow of parasites between populations, the spatial scale of malaria transmission is often not clear. As a result, it is important to understand the impact of varying assumptions about the spatial scale of transmission on key metrics of malaria transmission, such as reproduction numbers. We developed a method which allows the flexible integration of distance metrics (such as Euclidian distance, genetic distance or accessibility matrices) with temporal information into a single inference framework to infer malaria reproduction numbers. Twelve scenarios were defined, representing different assumptions about the likelihood of transmission occurring over different geographic distances and likelihood of missing infections (as well as high and low amounts of uncertainty in this estimate). These scenarios were applied to four individual level datasets from malaria eliminating contexts to estimate individual reproduction numbers and how they varied over space and time. Model comparison suggested that including spatial information improved models as measured by second order AIC (ΔAICc), compared to time only results. Across scenarios and across datasets, including spatial information tended to increase the seasonality of temporal patterns in reproduction numbers and reduced noise in the temporal distribution of reproduction numbers. The best performing parameterisations assumed long-range transmission (> 200 km) was possible. Our approach is flexible and provides the potential to incorporate other sources of information which can be converted into distance or adjacency matrices such as travel times or molecular markers.
    MeSH term(s) Basic Reproduction Number ; China/epidemiology ; El Salvador/epidemiology ; Eswatini/epidemiology ; Humans ; Malaria/epidemiology ; Malaria/transmission ; Malaria, Falciparum/epidemiology ; Malaria, Falciparum/transmission ; Malaria, Vivax/epidemiology ; Malaria, Vivax/transmission ; Travel
    Language English
    Publishing date 2021-07-14
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-021-93238-0
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  5. Article ; Online: Inference of malaria reproduction numbers in three elimination settings by combining temporal data and distance metrics

    Isobel Routledge / H. Juliette T. Unwin / Samir Bhatt

    Scientific Reports, Vol 11, Iss 1, Pp 1-

    2021  Volume 21

    Abstract: Abstract Individual-level geographic information about malaria cases, such as the GPS coordinates of residence or health facility, is often collected as part of surveillance in near-elimination settings, but could be more effectively utilised to infer ... ...

    Abstract Abstract Individual-level geographic information about malaria cases, such as the GPS coordinates of residence or health facility, is often collected as part of surveillance in near-elimination settings, but could be more effectively utilised to infer transmission dynamics, in conjunction with additional information such as symptom onset time and genetic distance. However, in the absence of data about the flow of parasites between populations, the spatial scale of malaria transmission is often not clear. As a result, it is important to understand the impact of varying assumptions about the spatial scale of transmission on key metrics of malaria transmission, such as reproduction numbers. We developed a method which allows the flexible integration of distance metrics (such as Euclidian distance, genetic distance or accessibility matrices) with temporal information into a single inference framework to infer malaria reproduction numbers. Twelve scenarios were defined, representing different assumptions about the likelihood of transmission occurring over different geographic distances and likelihood of missing infections (as well as high and low amounts of uncertainty in this estimate). These scenarios were applied to four individual level datasets from malaria eliminating contexts to estimate individual reproduction numbers and how they varied over space and time. Model comparison suggested that including spatial information improved models as measured by second order AIC (ΔAICc), compared to time only results. Across scenarios and across datasets, including spatial information tended to increase the seasonality of temporal patterns in reproduction numbers and reduced noise in the temporal distribution of reproduction numbers. The best performing parameterisations assumed long-range transmission (> 200 km) was possible. Our approach is flexible and provides the potential to incorporate other sources of information which can be converted into distance or adjacency matrices such as travel times or molecular ...
    Keywords Medicine ; R ; Science ; Q
    Subject code 550
    Language English
    Publishing date 2021-07-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Orphanhood and Caregiver Loss Among Children Based on New Global Excess COVID-19 Death Estimates.

    Hillis, Susan / N'konzi, Joel-Pascal Ntwali / Msemburi, William / Cluver, Lucie / Villaveces, Andrés / Flaxman, Seth / Unwin, H Juliette T

    JAMA pediatrics

    2022  Volume 176, Issue 11, Page(s) 1145–1148

    MeSH term(s) Child ; Humans ; Caregivers ; COVID-19 ; Acquired Immunodeficiency Syndrome ; Foster Home Care
    Language English
    Publishing date 2022-11-01
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2701223-2
    ISSN 2168-6211 ; 2168-6203
    ISSN (online) 2168-6211
    ISSN 2168-6203
    DOI 10.1001/jamapediatrics.2022.3157
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    Um IV Zs.74: Show issues Location:
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  7. Book ; Online: A comparison of short-term probabilistic forecasts for the incidence of COVID-19 using mechanistic and statistical time series models

    Banholzer, Nicolas / Mellan, Thomas / Unwin, H Juliette T / Feuerriegel, Stefan / Mishra, Swapnil / Bhatt, Samir

    2023  

    Abstract: Short-term forecasts of infectious disease spread are a critical component in risk evaluation and public health decision making. While different models for short-term forecasting have been developed, open questions about their relative performance remain. ...

    Abstract Short-term forecasts of infectious disease spread are a critical component in risk evaluation and public health decision making. While different models for short-term forecasting have been developed, open questions about their relative performance remain. Here, we compare short-term probabilistic forecasts of popular mechanistic models based on the renewal equation with forecasts of statistical time series models. Our empirical comparison is based on data of the daily incidence of COVID-19 across six large US states over the first pandemic year. We find that, on average, probabilistic forecasts from statistical time series models are overall at least as accurate as forecasts from mechanistic models. Moreover, statistical time series models better capture volatility. Our findings suggest that domain knowledge, which is integrated into mechanistic models by making assumptions about disease dynamics, does not improve short-term forecasts of disease incidence. We note, however, that forecasting is often only one of many objectives and thus mechanistic models remain important, for example, to model the impact of vaccines or the emergence of new variants.

    Comment: 37 pages, 4 Figures, 9 Appendix figures
    Keywords Statistics - Applications ; Computer Science - Machine Learning ; Quantitative Biology - Populations and Evolution ; Statistics - Machine Learning
    Publishing date 2023-05-01
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: Deep learning and MCMC with aggVAE for shifting administrative boundaries

    Semenova, Elizaveta / Mishra, Swapnil / Bhatt, Samir / Flaxman, Seth / Unwin, H Juliette T

    mapping malaria prevalence in Kenya

    2023  

    Abstract: Model-based disease mapping remains a fundamental policy-informing tool in the fields of public health and disease surveillance. Hierarchical Bayesian models have emerged as the state-of-the-art approach for disease mapping since they are able to both ... ...

    Abstract Model-based disease mapping remains a fundamental policy-informing tool in the fields of public health and disease surveillance. Hierarchical Bayesian models have emerged as the state-of-the-art approach for disease mapping since they are able to both capture structure in the data and robustly characterise uncertainty. When working with areal data, e.g.~aggregates at the administrative unit level such as district or province, current models rely on the adjacency structure of areal units to account for spatial correlations and perform shrinkage. The goal of disease surveillance systems is to track disease outcomes over time. This task is especially challenging in crisis situations which often lead to redrawn administrative boundaries, meaning that data collected before and after the crisis are no longer directly comparable. Moreover, the adjacency-based approach ignores the continuous nature of spatial processes and cannot solve the change-of-support problem, i.e.~when estimates are required to be produced at different administrative levels or levels of aggregation. We present a novel, practical, and easy to implement solution to solve these problems relying on a methodology combining deep generative modelling and fully Bayesian inference: we build on the recently proposed PriorVAE method able to encode spatial priors over small areas with variational autoencoders by encoding aggregates over administrative units. We map malaria prevalence in Kenya, a country in which administrative boundaries changed in 2010.
    Keywords Computer Science - Machine Learning ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2023-05-31
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Effectiveness assessment of non-pharmaceutical interventions: lessons learned from the COVID-19 pandemic.

    Lison, Adrian / Banholzer, Nicolas / Sharma, Mrinank / Mindermann, Sören / Unwin, H Juliette T / Mishra, Swapnil / Stadler, Tanja / Bhatt, Samir / Ferguson, Neil M / Brauner, Jan / Vach, Werner

    The Lancet. Public health

    2023  Volume 8, Issue 4, Page(s) e311–e317

    Abstract: Effectiveness of non-pharmaceutical interventions (NPIs), such as school closures and stay-at-home orders, during the COVID-19 pandemic has been assessed in many studies. Such assessments can inform public health policies and contribute to evidence-based ...

    Abstract Effectiveness of non-pharmaceutical interventions (NPIs), such as school closures and stay-at-home orders, during the COVID-19 pandemic has been assessed in many studies. Such assessments can inform public health policies and contribute to evidence-based choices of NPIs during subsequent waves or future epidemics. However, methodological issues and no standardised assessment practices have restricted the practical value of the existing evidence. Here, we present and discuss lessons learned from the COVID-19 pandemic and make recommendations for standardising and improving assessment, data collection, and modelling. These recommendations could contribute to reliable and policy-relevant assessments of the effectiveness of NPIs during future epidemics.
    MeSH term(s) Humans ; COVID-19 ; Pandemics/prevention & control ; Data Collection ; Public Policy ; Schools
    Language English
    Publishing date 2023-03-25
    Publishing country England
    Document type Journal Article ; Review ; Research Support, Non-U.S. Gov't
    ISSN 2468-2667
    ISSN (online) 2468-2667
    DOI 10.1016/S2468-2667(23)00046-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Estimating the Effect of Social Distancing Interventions on COVID-19 in the United States.

    Olney, Andrew M / Smith, Jesse / Sen, Saunak / Thomas, Fridtjof / Unwin, H Juliette T

    American journal of epidemiology

    2021  Volume 190, Issue 8, Page(s) 1504–1509

    Abstract: Since its global emergence in 2020, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused multiple epidemics in the United States. When medical treatments for the virus were still emerging and a vaccine was not yet available, state and ... ...

    Abstract Since its global emergence in 2020, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused multiple epidemics in the United States. When medical treatments for the virus were still emerging and a vaccine was not yet available, state and local governments sought to limit its spread by enacting various social-distancing interventions, such as school closures and lockdowns; however, the effectiveness of these interventions was unknown. We applied an established, semimechanistic Bayesian hierarchical model of these interventions to the spread of SARS-CoV-2 from Europe to the United States, using case fatalities from February 29, 2020, up to April 25, 2020, when some states began reversing their interventions. We estimated the effects of interventions across all states, contrasted the estimated reproduction numbers before and after lockdown for each state, and contrasted the predicted number of future fatalities with the actual number of fatalities as a check of the model's validity. Overall, school closures and lockdowns were the only interventions modeled that had a reliable impact on the time-varying reproduction number, and lockdown appears to have played a key role in reducing that number to below 1.0. We conclude that reversal of lockdown without implementation of additional, equally effective interventions will enable continued, sustained transmission of SARS-CoV-2 in the United States.
    MeSH term(s) Basic Reproduction Number ; Bayes Theorem ; COVID-19/epidemiology ; COVID-19/prevention & control ; Communicable Disease Control/methods ; Communicable Disease Control/statistics & numerical data ; Europe/epidemiology ; Humans ; Physical Distancing ; Quarantine/statistics & numerical data ; SARS-CoV-2 ; United States/epidemiology
    Language English
    Publishing date 2021-01-06
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2937-3
    ISSN 1476-6256 ; 0002-9262
    ISSN (online) 1476-6256
    ISSN 0002-9262
    DOI 10.1093/aje/kwaa293
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