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(A) Map of US Counties and their respective absolute humidity clusters. Each county is colored based on their cluster. Counties that are included in the regression analysis are indicated by a darker shade. The clustering analysis was conducted using a partitional algorithm that utilized dynamic time warping (DTW) to measure similarity between absolute humidity profiles of 3137 counties in the United States. Expectantly, the clustering of absolute humidity is related to the geography of the counties which serves as a proxy for regional weather patterns and different climatological regimes. (B) The cross-sectional smoothed mean of human encounter absolute humidity, and new case per 10,000 people trends for each cluster group of the 497 counties analyzed in the regression analysis. Map was generated using the ggplot package³¹ in R.
Source publication
Mounting evidence suggests the primary mode of SARS-CoV-2 transmission is aerosolized transmission from close contact with infected individuals. While transmission is a direct result of human encounters, falling humidity may enhance aerosolized transmission risks similar to other respiratory viruses (e.g., influenza). Using Google COVID-19 Communit...
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Background: It is well known that influenza and other respiratory viruses are wintertime-seasonal in temperate regions. However, respiratory disease seasonality in the tropics remains elusive. In this study, we aimed to characterize the seasonality of influenza-like illness (ILI) and influenza virus in Ho Chi Minh City (HCMC), Vietnam.
Methods: We...
Citations
... By contrast, we also found that when absolute humidity was low during the winter season, there was a significant decrease in COVID-19 hospitalizations. This runs counter to the findings of other studies which have suggested that cold, dry conditions are likely to promote COVID-19 transmission, particularly in winter and in drier climates [59]. Given the low levels of correlation between indoor and outdoor absolute humidity during the winter in our study (see Figs 3 and 4), our findings may suggest a mitigating effect of indoor climate controls. ...
There is growing evidence that weather alters SARS-CoV-2 transmission, but it remains unclear what drives the phenomenon. One prevailing hypothesis is that people spend more time indoors in cooler weather, leading to increased spread of SARS-CoV-2 related to time spent in confined spaces and close contact with others. However, the evidence in support of that hypothesis is limited and, at times, conflicting. We use a mediation framework, and combine daily weather, COVID-19 hospital surveillance, cellphone-based mobility data and building footprints to estimate the relationship between daily indoor and outdoor weather conditions, mobility, and COVID-19 hospitalizations. We quantify the direct health impacts of weather on COVID-19 hospitalizations and the indirect effects of weather via time spent indoors away-from-home on COVID-19 hospitalizations within five Colorado counties between March 4th 2020 and January 31st 2021. We also evaluated the evidence for seasonal effect modification by comparing the results of all-season (using season as a covariate) to season-stratified models. Four weather conditions were associated with both time spent indoors away-from-home and 12-day lagged COVID-19 hospital admissions in one or more season: high minimum temperature (all-season), low maximum temperature (spring), low minimum absolute humidity (winter), and high solar radiation (all-season & winter). In our mediation analyses, we found evidence that changes in 12-day lagged hospital admissions were primarily via the direct effects of weather conditions, rather than via indirect effects by which weather changes time spent indoors away-from-home. Our findings do not support the hypothesis that weather impacted SARS-CoV-2 transmission via changes in mobility patterns during the first year of the pandemic. Rather, weather appears to have impacted SARS-CoV-2 transmission primarily via mechanisms other than human movement. We recommend further analysis of this phenomenon to determine whether these findings generalize to current SARS-CoV-2 transmission dynamics, as well as other seasonal respiratory pathogens.
... While widespread immunity against SARS-CoV-2 has been achieved globally through vaccination and infections [2], the continued evolution of the virus causes antigenic changes and raises the potential for recurrent epidemics [3,4]. Current evidence suggests that both patterns of human contact and environmental factors contribute to seasonality in the intensity of SARS-CoV-2 transmission [5][6][7]. Combined, seasonality and ongoing "antigenic drift (i.e., gradual genetic changes in a virus evading prior population immunity [8])" of SARS-CoV-2 make it highly likely that the virus will pose a persistent threat to public health for the foreseeable future. Going forward, one of the main tools for mitigating the impact of annual COVID-19 epidemics will be vaccination. ...
Background
Coronavirus Disease 2019 (COVID-19) continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Here, we present projections of COVID-19 hospitalizations and deaths in the United States for the next 2 years under 2 plausible assumptions about immune escape (20% per year and 50% per year) and 3 possible CDC recommendations for the use of annually reformulated vaccines (no recommendation, vaccination for those aged 65 years and over, vaccination for all eligible age groups based on FDA approval).
Methods and findings
The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023 and April 15, 2025 under 6 scenarios representing the intersection of considered levels of immune escape and vaccination. Annually reformulated vaccines are assumed to be 65% effective against symptomatic infection with strains circulating on June 15 of each year and to become available on September 1. Age- and state-specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. State and national projections from 8 modeling teams were ensembled to produce projections for each scenario and expected reductions in disease outcomes due to vaccination over the projection period.
From April 15, 2023 to April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November to January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% projection interval (PI) [1,438,000, 4,270,000]) hospitalizations and 209,000 (90% PI [139,000, 461,000]) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% confidence interval (CI) [104,000, 355,000]) fewer hospitalizations and 33,000 (95% CI [12,000, 54,000]) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000–598,000) fewer hospitalizations and 49,000 (95% CI [29,000, 69,000]) fewer deaths.
Conclusions
COVID-19 is projected to be a significant public health threat over the coming 2 years. Broad vaccination has the potential to substantially reduce the burden of this disease, saving tens of thousands of lives each year.
... Similar to other respiratory viruses, high humidity is expected to enhance the transmission risk of SARS-CoV-2. However, increasing humidity was associated with declining COVID-19 cases in the spring and summer, while decreasing humidity and increasing residential mobility during the winter months caused an increase in COVID-19 cases [22]. It has been reported that the attenuation of SARS-CoV-2 due to temperature is minimal [23,24]. ...
One of the methods to inactivate viruses is to denature viral proteins using released ions. However, there have been no reports detailing the effects of changes in humidity or contamination with body fluids on the inactivation of viruses. This study investigated the effects of humidity changes and saliva contamination on the efficacy of SARS-CoV-2 inactivation with ions using multiple viral strains. Virus solutions with different infectious titers were dropped onto a circular nitrocellulose membrane and irradiated with ions from 10 cm above the membrane. After the irradiation of ions for 60, 90, and 120 min, changes in viral infectious titers were measured. The effect of ions on virus inactivation under different humidity conditions was also examined using virus solutions containing 90% mixtures of saliva collected from 10 people. A decrease in viral infectivity was observed over time for all strains, but ion irradiation further accelerated the decrease in viral infectivity. Ion irradiation can inactivate all viral strains, but at 80% humidity, the effect did not appear until 90 min after irradiation. The presence of saliva protected the virus from drying and maintained infectiousness for a longer period compared with no saliva. In particular, the Omicron strain retained its infectivity titer longer than the other strains. Ion irradiation demonstrated a consistent reduction in the number of infectious viruses when compared to the control across varying levels of humidity and irradiation periods. This underscores the notable effectiveness of irradiation, even when the reduction effect is as modest as 50%, thereby emphasizing its crucial role in mitigating the rapid dissemination of SARS-CoV-2.
... We took advantage of the data publicly available from epidemiological surveillance systems on respiratory viruses, in the USA and in Canada, and tested the effects of weather and of mobility (as a relevant component of behaviour [49][50][51][52][53][54]), in viral dynamics before and after the onset of the COVID-19 pandemic. By comparing their contributions over time, we were able to better analyse their respective weights in the pre-pandemic and pandemic periods. ...
... Overall, mobility data has been shown to be a good proxy for the strength of lockdown measures and other NPIs [49,50] and has been increasingly available and used in recent years as an indicator of behavioural changes affecting infection dynamics (e.g. [51][52][53][54]). ...
The flu season is caused by a combination of different pathogens, including influenza viruses (IVS), that cause the flu, and non-influenza respiratory viruses (NIRVs), that cause common colds or influenza-like illness. These viruses exhibit similar dynamics and meteorological conditions have historically been regarded as a principal modulator of their epidemiology, with outbreaks in the winter and almost no circulation during the summer, in temperate regions. However, after the emergence of SARS-CoV2, in late 2019, the dynamics of these respiratory viruses were strongly perturbed worldwide: some infections displayed near-eradication, while others experienced temporal shifts or occurred “off-season”. This disruption raised questions regarding the dominant role of weather while also providing an unique opportunity to investigate the roles of different determinants on the epidemiological dynamics of IVs and NIRVs. Here, we employ statistical analysis and modelling to test the effects of weather and mobility in viral dynamics, before and during the COVID-19 pandemic. Leveraging epidemiological surveillance data on several respiratory viruses, from Canada and the USA, from 2016 to 2023, we found that whereas in the pre-COVID-19 pandemic period, weather had a strong effect, in the pandemic period the effect of weather was strongly reduced and mobility played a more relevant role. These results, together with previous studies, indicate that behavioral changes resulting from the non-pharmacological interventions implemented to control SARS-CoV2, interfered with the dynamics of other respiratory viruses, and that the past dynamical equilibrium was disturbed, and perhaps permanently altered, by the COVID-19 pandemic.
... While widespread immunity against SARS-CoV-2 has been achieved globally through vaccination and infections [2], the continued evolution of the virus causes antigenic changes and raises the potential for recurrent epidemics [3,4]. Current evidence suggests that both patterns of human contact and environmental factors contribute to seasonality in the intensity of SARS-CoV-2 transmission [5][6][7]. Combined, seasonality and ongoing "antigenic drift" of SARS-CoV-2 make it highly likely that the virus will pose a persistent threat to public health for the foreseeable future. ...
Importance: COVID-19 continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear.
Objective: To project COVID-19 hospitalizations and deaths from April 2023–April 2025 under two plausible assumptions about immune escape (20% per year and 50% per year) and three possible CDC recommendations for the use of annually reformulated vaccines (no vaccine recommendation, vaccination for those aged 65+, vaccination for all eligible groups).
Design: The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023–April 15, 2025 under six scenarios representing the intersection of considered levels of immune escape and vaccination. State and national projections from eight modeling teams were ensembled to produce projections for each scenario.
Setting: The entire United States.
Participants: None.
Exposure: Annually reformulated vaccines assumed to be 65% effective against strains circulating on June 15 of each year and to become available on September 1. Age and state specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster.
Main outcomes and measures: Ensemble estimates of weekly and cumulative COVID-19 hospitalizations and deaths. Expected relative and absolute reductions in hospitalizations and deaths due to vaccination over the projection period.
Results: From April 15, 2023–April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November–January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% PI: 1,438,000–4,270,000) hospitalizations and 209,000 (90% PI: 139,000–461,000) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% CI: 104,000–355,000) fewer hospitalizations and 33,000 (95% CI: 12,000–54,000) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000–598,000) fewer hospitalizations and 49,000 (95% CI: 29,000–69,000) fewer deaths.
Conclusion and Relevance: COVID-19 is projected to be a significant public health threat over the coming two years. Broad vaccination has the potential to substantially reduce the burden of this disease.
... In addition, larger plants produce more water vapor 4 . When a source continuously releases large amounts of wet-components, humidity increases rapidly, which can produce dew and mold on the walls 5,6 , and cause respiratory discomfort and allergies [7][8][9] . When the humidity is overly low (≤30%) due to the sources absorbing wet-components, dryness will not only affect the thermal comfort of occupants 10 but also cause respiratory pain 11,12 , eye itching [13][14][15] and static electricity. ...
Moisture sources release wet-components into indoor air, affecting the occupants’ health, air conditioning energy consumption, and building service-life. Wet-component evaporation and diffusion are dynamic processes, and yet existing indices are limited in their ability to accurately describe moisture sources dynamically influencing indoor air. Here we propose two indices CRI t (H), an index of the rate of humidity contribution change, and CRI t (c) as the rate of indoor climate contribution change. Taking a humidifier as the source, we use our indices to compare by experiment the impact of source parameters on a variety of ambient conditions over space and time. Our approach accurately reflects how the moisture source affect humidity and temperature, with identification of specific stages of dynamic influence. This study will be beneficial for the establishment of transient indoor environmental models, regulation of air-conditioning systems, and sustainable control of the indoor environment.
... However, further analysis considering these locations is warranted if these data are available. The current study also did not include meteorological factors such as temperature and humidity as explanatory variables, which have been suggested to have negative impact on COVID-19 transmission (11,22) Because these meteorological factors may also exhibit interactions with social contact patterns, we excluded them from consideration. Risk awareness was also considered in previous studies (11) but is difficult to quantify. ...
Background
Mobility data are crucial for understanding the dynamics of coronavirus disease 2019 (COVID-19), but the consistency of the usefulness of these data over time has been questioned. The present study aimed to reveal the relationship between the transmissibility of COVID-19 in Tokyo, Osaka, and Aichi prefectures and the daily night-time population in metropolitan areas belonging to each prefecture.
Methods
In Japan, the de facto population estimated from GPS-based location data from mobile phone users is regularly monitored by Ministry of Health, Labor, and Welfare and other health departments. Combined with this data, we conducted a time series linear regression analysis to explore the relationship between daily reported case counts of COVID-19 in Tokyo, Osaka, and Aichi, and night-time de facto population in downtown areas estimated from mobile phone location data, from February 2020 to May 2022. As an approximation of the effective reproduction number, the weekly ratio of cases was used. Models using night-time population with lags ranging from 7 to 14 days were tested. In time-varying regression analysis, the night-time population level and the daily change in night-time population level were included as explanatory variables. In the fixed-effect regression analysis, the inclusion of either the night-time population level or daily change, or both, as explanatory variables was tested, and autocorrelation was adjusted by introducing first-order autoregressive error of residuals. In both regression analyses, the lag of night-time population used in best fit models was determined using the information criterion.
Results
In the time-varying regression analysis, night-time population level tended to show positive to neutral effects on COVID-19 transmission, whereas the daily change of night-time population showed neutral to negative effects. The fixed-effect regression analysis revealed that for Tokyo and Osaka, regression models with 8-day-lagged night-time population level and daily change were the best fit, whereas in Aichi, the model using only the 9-day-lagged night-time population level was the best fit using the widely applicable information criterion. For all regions, the best-fit model suggested a positive relationship between night-time population and transmissibility, which was maintained over time.
Conclusion
Our results revealed that, regardless of the period of interest, a positive relationship between night-time population levels and COVID-19 dynamics was observed. The introduction of vaccinations and major outbreaks of Omicron BA. Two subvariants in Japan did not dramatically change the relationship between night-time population and COVID-19 dynamics in three megacities in Japan. Monitoring the night-time population continues to be crucial for understanding and forecasting the short-term future of COVID-19 incidence.
... 70 For both countries, the defined pre-COVID-19 pandemic period starts at data collection and ends on the 71 week of 07 th March, 2020, and the COVID-19 pandemic period starts on the following week, of the 14 th 72 March, 2020, to include the date when the COVID-19 pandemic was declared by the WHO (see Fig. 1). 73 ILI outpatient rates (number of outpatients visits to sentinel physicians due to ILI divided by the total 74 number of visits) were collected using weekly, nation-wide ILI data from the U.S. Outpatient Influenza-like 75 Illness Surveillance Network (ILINet, CDC) in the case of USA [52] and from the Syndromic/Influenza-like 76 Illness Surveillance (FluWatch, CIRID) in the case of Canada [53]. 77 Incidence was defined as the product of the weekly virus-specific positivity rate and the weekly ILI-78 outpatient rate, per 1000 people, and this rate is referred to by incidence, throughout the text. ...
... Mobility has been shown to be a good 330 indicator for the strength of lockdown measures and other NPIs [70,71] and has been increasingly used in 331 recent years as an indicator of behavioural changes affecting infection dynamics (e.g. [72][73][74][75]). Therefore, 332 our results that indicate that mobility better explains the observed dynamics, are in line with previous 333 evidence suggesting that the behavioral changes and the non-pharmacological interventions resulting from 334 the COVID-19 pandemic had a significant effect on the epidemiology of these viruses. ...
The flu season is caused by a combination of different pathogens, including influenza viruses (IVS), that cause the flu, and non-influenza respiratory viruses (NIRVs), that cause common colds or influenza-like illness. These viruses have similar circulation patterns, and weather has been considered a main driver of their dynamics, with peaks in the winter and almost no circulation during the summer in temperate regions. However, after the emergence of SARS-CoV2, in 2019, the dynamics of these respiratory viruses were strongly perturbed worldwide: some infections almost disappeared, others were delayed or occurred "off-season".
This disruption raised questions regarding the dominant role of weather while also providing an unique opportunity to investigate the relevance of different driving factors on the epidemiological dynamics of IVs and NIRVs, including viral interactions, non-pharmacological individual measures (such as masking), or mobility.
Here, we use epidemiological surveillance data on several respiratory viruses from Canada and the USA from 2016 to 2023, and tested the effects of weather and mobility in their dynamics before and after the COVID-19 pandemic. Using statistical modelling, we found evidence that whereas in the pre-COVID-19 pandemic period, weather had a strong effect and mobility a limited effect on dynamics; in the post-COVID-19 pandemic period the effect of weather was strongly reduced and mobility played a more relevant role.
These results, together with previous studies, indicate that at least some of the behavioral changes resulting from the non-pharmacological interventions implemented during COVID-19 pandemic had a strong effect on the dynamics of respiratory viruses. Furthermore, our results support the idea that these seasonal dynamics are driven by a complex system of interactions between the different factors involved, which probably led to an equilibrium that was disturbed, and perhaps permanently altered, by the COVID-19 pandemic.
... Data on average 2017-2019 prescriptions by month were included to control for county-level prescribing behavior, and calendar month was included to capture seasonal differences in prescribing trends [13], climatological factors (eg, absolute humidity) associated with increases in URIs [32,33], and temporal variations in government responses and pandemicrelated behavioral changes. Because children likely had the greatest change in contact patterns during the pandemic with the closing of schools, we conducted a subanalysis with the same model except that the outcome variable was the number of prescriptions dispensed to children aged 0-9. ...
Background
Declines in outpatient antibiotic prescribing were reported during the beginning of the coronavirus disease 2019 (COVID-19) pandemic in the United States; however, the overall impact of COVID-19 cases on antibiotic prescribing remains unclear.
Methods
This was an ecological study using random-effects panel regression of monthly reported COVID-19 county case and antibiotic prescription data, controlling for seasonality, urbanicity, health care access, nonpharmaceutical interventions (NPIs), and sociodemographic factors.
Results
Antibiotic prescribing fell 26.8% in 2020 compared with prior years. Each 1% increase in county-level monthly COVID-19 cases was associated with a 0.009% (95% CI, 0.007% to 0.012%; P < .01) increase in prescriptions per 100 000 population dispensed to all ages and a 0.012% (95% CI, −0.017% to −0.008%; P < .01) decrease in prescriptions per 100 000 children. Counties with schools open for in-person instruction were associated with a 0.044% (95% CI, 0.024% to 0.065%; P < .01) increase in prescriptions per 100 000 children compared with counties that closed schools. Internal movement restrictions and requiring facemasks were also associated with lower prescribing among children.
Conclusions
The positive association of COVID-19 cases with prescribing for all ages and the negative association for children indicate that increases in prescribing occurred primarily among adults. The rarity of bacterial coinfection in COVID-19 patients suggests that a fraction of these prescriptions may have been inappropriate. Facemasks and school closures were correlated with reductions in prescribing among children, possibly due to the prevention of other upper respiratory infections. The strongest predictors of prescribing were prior years’ prescribing trends, suggesting the possibility that behavioral norms are an important driver of prescribing practices.
... Finally, contrary to previous beliefs, the surface transmission of SARS-COV-2 is now thought to be very low (42,43). Recent COVID-19 studies in the United States show a negative correlation between humidity and cases in (30), the same as in Australia (44), and Bangladesh (29). Water (relative humidity, precipitation total, and column of water vapor) has also been shown to play a regulatory role in the transmission of COVID-19 in the southern hemisphere (27). ...
