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Article ; Online: Reconciling the efficacy and effectiveness of masking on epidemic outcomes.

Yang, Wan / Shaman, Jeffrey

2024 Volume 21, Issue 212, Page(s) 20230666

Abstract: During the COVID-19 pandemic, mask wearing in public settings has been a key control measure. However, the reported effectiveness of masking has been much lower than laboratory measures of efficacy, leading to doubts on the utility of masking. Here, we ... ...

Abstract	During the COVID-19 pandemic, mask wearing in public settings has been a key control measure. However, the reported effectiveness of masking has been much lower than laboratory measures of efficacy, leading to doubts on the utility of masking. Here, we develop an agent-based model that comprehensively accounts for individual masking behaviours and infectious disease dynamics, and test the impact of masking on epidemic outcomes. Using realistic inputs of mask efficacy and contact data at the individual level, the model reproduces the lower effectiveness as reported in randomized controlled trials. Model results demonstrate that transmission within households, where masks are rarely used, can substantially lower effectiveness, and reveal the interaction of nonlinear epidemic dynamics, control measures and potential measurement biases. Overall, model results show that, at the individual level, consistent masking can reduce the risk of first infection and, over time, reduce the frequency of repeated infection. At the population level, masking can provide direct protection to mask wearers, as well as indirect protection to non-wearers, collectively reducing epidemic intensity. These findings suggest it is prudent for individuals to use masks during an epidemic, and for policymakers to recognize the less-than-ideal effectiveness of masking when devising public health interventions.
MeSH term(s)	Humans ; Pandemics/prevention & control ; COVID-19/epidemiology ; COVID-19/prevention & control ; Nonlinear Dynamics ; Public Health
Language	English
Publishing date	2024-03-06
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, P.H.S.
ZDB-ID	2156283-0
ISSN	1742-5662 ; 1742-5689
ISSN (online)	1742-5662
ISSN	1742-5689
DOI	10.1098/rsif.2023.0666
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Article ; Online: An estimation of undetected COVID cases in France.

Shaman, Jeffrey

Nature

2020 Volume 590, Issue 7844, Page(s) 38–39

MeSH term(s)	COVID-19 ; Epidemics ; France/epidemiology ; Humans ; SARS-CoV-2
Language	English
Publishing date	2020-12-21
Publishing country	England
Document type	News ; Comment
ZDB-ID	120714-3
ISSN	1476-4687 ; 0028-0836
ISSN (online)	1476-4687
ISSN	0028-0836
DOI	10.1038/d41586-020-03513-9
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Article ; Online: Development of Accurate Long-lead COVID-19 Forecast.

Yang, Wan / Shaman, Jeffrey

PLoS computational biology

2023 Volume 19, Issue 7, Page(s) e1011278

Abstract: Coronavirus disease 2019 (COVID-19) will likely remain a major public health burden; accurate forecast of COVID-19 epidemic outcomes several months into the future is needed to support more proactive planning. Here, we propose strategies to address three ...

Abstract	Coronavirus disease 2019 (COVID-19) will likely remain a major public health burden; accurate forecast of COVID-19 epidemic outcomes several months into the future is needed to support more proactive planning. Here, we propose strategies to address three major forecast challenges, i.e., error growth, the emergence of new variants, and infection seasonality. Using these strategies in combination we generate retrospective predictions of COVID-19 cases and deaths 6 months in the future for 10 representative US states. Tallied over >25,000 retrospective predictions through September 2022, the forecast approach using all three strategies consistently outperformed a baseline forecast approach without these strategies across different variant waves and locations, for all forecast targets. Overall, probabilistic forecast accuracy improved by 64% and 38% and point prediction accuracy by 133% and 87% for cases and deaths, respectively. Real-time 6-month lead predictions made in early October 2022 suggested large attack rates in most states but a lower burden of deaths than previous waves during October 2022 -March 2023; these predictions are in general accurate compared to reported data. The superior skill of the forecast methods developed here demonstrate means for generating more accurate long-lead forecast of COVID-19 and possibly other infectious diseases.
MeSH term(s)	Humans ; COVID-19/epidemiology ; Retrospective Studies ; Epidemics ; Incidence ; Forecasting
Language	English
Publishing date	2023-07-17
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, P.H.S. ; 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.1011278
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Article: Development of Accurate Long-lead COVID-19 Forecast.

Yang, Wan / Shaman, Jeffrey

medRxiv : the preprint server for health sciences

2023

Abstract	Coronavirus disease 2019 (COVID-19) will likely remain a major public health burden; accurate forecast of COVID-19 epidemic outcomes several months into the future is needed to support more proactive planning. Here, we propose strategies to address three major forecast challenges, i.e., error growth, the emergence of new variants, and infection seasonality. Using these strategies in combination we generate retrospective predictions of COVID-19 cases and deaths 6 months in the future for 10 representative US states. Tallied over >25,000 retrospective predictions through September 2022, the forecast approach using all three strategies consistently outperformed a baseline forecast approach without these strategies across different variant waves and locations, for all forecast targets. Overall, probabilistic forecast accuracy improved by 64% and 38% and point prediction accuracy by 133% and 87% for cases and deaths, respectively. Real-time 6-month lead predictions made in early October 2022 suggested large attack rates in most states but a lower burden of deaths than previous waves during October 2022 - March 2023; these predictions are in general accurate compared to reported data. The superior skill of the forecast methods developed here demonstrate means for generating more accurate long-lead forecast of COVID-19 and possibly other infectious diseases. Author summary: Infectious disease forecast aims to reliably predict the most likely future outcomes during an epidemic. To date, reliable COVID-19 forecast remains elusive and is needed to support more proactive planning. Here, we pinpoint the major challenges facing COVID-19 forecast and propose three strategies. Comprehensive testing shows the forecast approach using all three strategies consistently outperforms a baseline approach without these strategies across different variant waves and locations in the United States for all forecast targets, improving the probabilistic forecast accuracy by ∼50% and point prediction accuracy by ∼100%. The superior skills of the forecast methods developed here demonstrate means for generating more accurate long-lead COVID-19 forecasts. The methods may be also applicable to other infectious diseases. One sentence summary: To support more proactive planning, we develop COVID-19 forecast methods that substantially improve accuracy with lead time up to 6 months.
Language	English
Publishing date	2023-04-12
Publishing country	United States
Document type	Preprint
DOI	10.1101/2022.11.14.22282323
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Article ; Online: Waterborne Infectious Diseases Associated with Exposure to Tropical Cyclonic Storms, United States, 1996-2018.

Lynch, Victoria D / Shaman, Jeffrey

Emerging infectious diseases

2023 Volume 29, Issue 8, Page(s) 1548–1558

Abstract: In the United States, tropical cyclones cause destructive flooding that can lead to adverse health outcomes. Storm-driven flooding contaminates environmental, recreational, and drinking water sources, but few studies have examined effects on specific ... ...

Abstract	In the United States, tropical cyclones cause destructive flooding that can lead to adverse health outcomes. Storm-driven flooding contaminates environmental, recreational, and drinking water sources, but few studies have examined effects on specific infections over time. We used 23 years of exposure and case data to assess the effects of tropical cyclones on 6 waterborne diseases in a conditional quasi-Poisson model. We separately defined storm exposure for windspeed, rainfall, and proximity to the storm track. Exposure to storm-related rainfall was associated with a 48% (95% CI 27%-69%) increase in Shiga toxin-producing Escherichia coli infections 1 week after storms and a 42% (95% CI 22%-62%) in increase Legionnaires' disease 2 weeks after storms. Cryptosporidiosis cases increased 52% (95% CI 42%-62%) during storm weeks but declined over ensuing weeks. Cyclones are a risk to public health that will likely become more serious with climate change and aging water infrastructure systems.
MeSH term(s)	Humans ; United States/epidemiology ; Cyclonic Storms ; Waterborne Diseases/epidemiology ; Cryptosporidiosis ; Legionnaires' Disease ; Communicable Diseases
Language	English
Publishing date	2023-07-24
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1380686-5
ISSN	1080-6059 ; 1080-6040
ISSN (online)	1080-6059
ISSN	1080-6040
DOI	10.3201/eid2908.221906
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Article ; Online: Work accidents, climate change and COVID-19.

Santurtún, Ana / Shaman, Jeffrey

The Science of the total environment

2023 Volume 871, Page(s) 162129

Abstract: The effects brought by climate change and the pandemic upon worker health and wellbeing are varied and necessitate the identification and implementation of improved strategic interventions. This review aims, firstly, to assess how climate change affects ... ...

Abstract	The effects brought by climate change and the pandemic upon worker health and wellbeing are varied and necessitate the identification and implementation of improved strategic interventions. This review aims, firstly, to assess how climate change affects occupational accidents, focusing on the impacts of extreme air temperatures and natural disasters; and, secondly, to analyze the role of the pandemic in this context. Our results show that the manifestations of climate change affect workers physically while on the job, psychologically, and by modifying the work environment and conditions; all these factors can cause stress, in turn increasing the risk of suffering a work accident. There is no consensus on the impact of the COVID-19 pandemic on work accidents; however, an increase in adverse mental effects on workers in contact with the public (specifically in healthcare) has been described. It has also been shown that this strain affects the risk of suffering an accident. During the pandemic, many people began to work remotely, and what initially appeared to be a provisional situation has been made permanent or semi-permanent in some positions and companies. However, we found no studies evaluating the working conditions of those who telework. In relation to the combined impact of climate change and the pandemic on occupational health, only publications focusing on the synergistic effect of heat due to the obligation to wear COVID-19-specific PPE, either outdoors or in poorly acclimatized indoor environments, were found. It is essential that preventive services establish new measures, train workers, and determine new priorities for adapting working conditions to these altered circumstances.
MeSH term(s)	Humans ; COVID-19/epidemiology ; Climate Change ; Pandemics ; Occupational Health ; Accidents
Language	English
Publishing date	2023-02-10
Publishing country	Netherlands
Document type	Journal Article ; Review
ZDB-ID	121506-1
ISSN	1879-1026 ; 0048-9697
ISSN (online)	1879-1026
ISSN	0048-9697
DOI	10.1016/j.scitotenv.2023.162129
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Article ; Online: Letter to the Editor: Caution needed when using gridded meteorological data products for analyses in Africa.

Shaman, J

Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin

2014 Volume 19, Issue 41

MeSH term(s)	Climate ; Disease Outbreaks ; Ebolavirus/isolation & purification ; Hemorrhagic Fever, Ebola/epidemiology ; Humans ; Humidity ; Temperature
Language	English
Publishing date	2014-10-16
Publishing country	Sweden
Document type	Letter ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S. ; Comment
ZDB-ID	1338803-4
ISSN	1560-7917 ; 1025-496X
ISSN (online)	1560-7917
ISSN	1025-496X
DOI	10.2807/1560-7917.es2014.19.41.20930
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Article ; Online: Pandemic preparedness and forecast.

Shaman, Jeffrey

Nature microbiology

2018 Volume 3, Issue 3, Page(s) 265–267

MeSH term(s)	Electronic Health Records ; Forecasting ; Hemorrhagic Fever, Ebola/epidemiology ; Hemorrhagic Fever, Ebola/prevention & control ; Humans ; Influenza, Human/epidemiology ; Influenza, Human/prevention & control ; Pandemics/prevention & control ; Public Health Surveillance ; Risk
Keywords	covid19
Language	English
Publishing date	2018-04-02
Publishing country	England
Document type	Journal Article
ISSN	2058-5276
ISSN (online)	2058-5276
DOI	10.1038/s41564-018-0117-7
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Article ; Online: The effect of seasonal and extreme floods on hospitalizations for Legionnaires' disease in the United States, 2000-2011.

Lynch, Victoria D / Shaman, Jeffrey

BMC infectious diseases

2022 Volume 22, Issue 1, Page(s) 550

Abstract: Background: An increasing severity of extreme storms and more intense seasonal flooding are projected consequences of climate change in the United States. In addition to the immediate destruction caused by storm surges and catastrophic flooding, these ... ...

Abstract	Background: An increasing severity of extreme storms and more intense seasonal flooding are projected consequences of climate change in the United States. In addition to the immediate destruction caused by storm surges and catastrophic flooding, these events may also increase the risk of infectious disease transmission. We aimed to determine the association between extreme and seasonal floods and hospitalizations for Legionnaires' disease in 25 US states during 2000-2011. Methods: We used a nonparametric bootstrap approach to examine the association between Legionnaires' disease hospitalizations and extreme floods, defined by multiple hydrometeorological variables. We also assessed the effect of extreme flooding associated with named cyclonic storms on hospitalizations in a generalized linear mixed model (GLMM) framework. To quantify the effect of seasonal floods, we used multi-model inference to identify the most highly weighted flood-indicator variables and evaluated their effects on hospitalizations in a GLMM. Results: We found a 32% increase in monthly hospitalizations at sites that experienced cyclonic storms, compared to sites in months without storms. Hospitalizations in months with extreme precipitation were in the 89 Conclusions: This analysis is the first to examine the effects of flooding on hospitalizations for Legionnaires' disease in the United States using a range of flood-indicator variables and flood definitions. We found evidence that extreme and seasonal flooding is associated with increased hospitalizations; further research is required to mechanistically establish whether floodwaters contaminated with Legionella bacteria drive transmission.
MeSH term(s)	Floods ; Hospitalization ; Humans ; Legionnaires' Disease/epidemiology ; Legionnaires' Disease/microbiology ; Seasons ; Soil ; United States/epidemiology
Chemical Substances	Soil
Language	English
Publishing date	2022-06-15
Publishing country	England
Document type	Journal Article
ZDB-ID	2041550-3
ISSN	1471-2334 ; 1471-2334
ISSN (online)	1471-2334
ISSN	1471-2334
DOI	10.1186/s12879-022-07489-x
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Article: COVID-19 pandemic dynamics in South Africa and epidemiological characteristics of three variants of concern (Beta, Delta, and Omicron).

Yang, Wan / Shaman, Jeffrey

medRxiv : the preprint server for health sciences

2022

Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) have been key drivers of new coronavirus disease 2019 (COVID-19) pandemic waves. To better understand variant epidemiologic characteristics, here we apply a model- ... ...

Abstract	Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) have been key drivers of new coronavirus disease 2019 (COVID-19) pandemic waves. To better understand variant epidemiologic characteristics, here we apply a model-inference system to reconstruct SARS-CoV-2 transmission dynamics in South Africa, a country that has experienced three VOC pandemic waves (i.e. Beta, Delta, and Omicron). We estimate key epidemiologic quantities in each of the nine South African provinces during March 2020 â€" Feb 2022, while accounting for changing detection rates, infection seasonality, nonpharmaceutical interventions, and vaccination. Model validation shows that estimated underlying infection rates and key parameters (e.g., infection-detection rate and infection-fatality risk) are in line with independent epidemiological data and investigations. In addition, retrospective predictions capture pandemic trajectories beyond the model training period. These detailed, validated model-inference estimates thus enable quantification of both the immune erosion potential and transmissibility of three major SARS-CoV-2 VOCs, i.e., Beta, Delta, and Omicron. These findings help elucidate changing COVID-19 dynamics and inform future public health planning.
Language	English
Publishing date	2022-06-29
Publishing country	United States
Document type	Preprint
DOI	10.1101/2021.12.19.21268073
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