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Journal of Theoretical Biology Jul 2022The many respiratory viruses that cause influenza-like illness (ILI) are reported and tracked as one entity, defined by the CDC as a group of symptoms that include a...
The many respiratory viruses that cause influenza-like illness (ILI) are reported and tracked as one entity, defined by the CDC as a group of symptoms that include a fever of 100 degrees Fahrenheit, a cough, and/or a sore throat. In the United States alone, ILI impacts 9-49 million people every year. While tracking ILI as a single clinical syndrome is informative in many respects, the underlying viruses differ in parameters and outbreak properties. Most existing models treat either a single respiratory virus or ILI as a whole. However, there is a need for models capable of comparing several individual viruses that cause respiratory illness, including ILI. To address this need, here we present a flexible model and simulations of epidemics for influenza, RSV, rhinovirus, seasonal coronavirus, adenovirus, and SARS/MERS, parameterized by a systematic literature review and accompanied by a global sensitivity analysis. We find that for these biological causes of ILI, their parameter values, timing, prevalence, and proportional contributions differ substantially. These results demonstrate that distinguishing the viruses that cause ILI will be an important aspect of future work on diagnostics, mitigation, modeling, and preparation for future pandemics.
Topics: Epidemics; Humans; Influenza, Human; Rhinovirus; Virus Diseases; Viruses
PubMed: 35490763
DOI: 10.1016/j.jtbi.2022.111145 -
PloS One 2021Intensity and duration of the COVID-19 pandemic, and planning required to balance concerns of saving lives and avoiding economic collapse, could depend significantly on...
IMPORTANCE
Intensity and duration of the COVID-19 pandemic, and planning required to balance concerns of saving lives and avoiding economic collapse, could depend significantly on whether SARS-CoV-2 transmission is sensitive to seasonal changes.
OBJECTIVE
Hypothesis is that increasing temperature results in reduced SARS CoV-2 transmission and may help slow the increase of cases over time.
SETTING
Fifty representative Northern Hemisphere countries meeting specific criteria had sufficient COVID-19 case and meteorological data for analysis.
METHODS
Regression was used to find the relationship between the log of number of COVID-19 cases and temperature over time in 50 representative countries. To summarize the day-day variability, and reduce dimensionality, we selected a robust measure, Coefficient of Time (CT), for each location. The resulting regression coefficients were then used in a multivariable regression against meteorological, country-level and demographic covariates.
RESULTS
Median minimum daily temperature showed the strongest correlation with the reciprocal of CT (which can be considered as a rate associated with doubling time) for confirmed cases (adjusted R2 = 0.610, p = 1.45E-06). A similar correlation was found using median daily dewpoint, which was highly colinear with temperature, and therefore was not used in the analysis. The correlation between minimum median temperature and the rate of increase of the log of confirmed cases was 47% and 45% greater than for cases of death and recovered cases of COVID-19, respectively. This suggests the primary influence of temperature is on SARS-CoV-2 transmission more than COVID-19 morbidity. Based on the correlation between temperature and the rate of increase in COVID-19, it can be estimated that, between the range of 30 to 100 degrees Fahrenheit, a one degree increase is associated with a 1% decrease-and a one degree decrease could be associated with a 3.7% increase-in the rate of increase of the log of daily confirmed cases. This model of the effect of decreasing temperatures can only be verified over time as the pandemic proceeds through colder months.
CONCLUSIONS
The results suggest that boreal summer months are associated with slower rates of COVID-19 transmission, consistent with the behavior of a seasonal respiratory virus. Knowledge of COVID-19 seasonality could prove useful in local planning for phased reductions social interventions and help to prepare for the timing of possible pandemic resurgence during cooler months.
Topics: COVID-19; Hot Temperature; Humans; Meteorological Concepts; Pandemics; SARS-CoV-2; Seasons; Weather
PubMed: 33596214
DOI: 10.1371/journal.pone.0246167 -
Case Reports in Infectious Diseases 2020. , an anaerobic Gram-negative bacillus, is a rare cause of opportunistic infections affecting premature infants to seniors. We present a 34-year-old man who was...
. , an anaerobic Gram-negative bacillus, is a rare cause of opportunistic infections affecting premature infants to seniors. We present a 34-year-old man who was presented for the management of diabetic ketoacidosis and developed bacteremia after one week of hospitalization. . A 34-year-old African-American male with uncontrolled diabetes mellitus type I and recurrent skin infections was admitted with diabetic ketoacidosis. He had left upper extremity abscess, preliminary wound cultures were positive for Gram-positive cocci, and an initial set of blood cultures were negative. He was started empirically on vancomycin. One week after admission, he started having chills followed by a recurrent increase in body temperature to 102 degrees Fahrenheit. The wound was healing, without active infection. Chest X-ray and CT scan of abdomen and pelvis to rule out infection were negative. Repeat blood cultures showed in both the tubes. The patient was successfully treated with intravenous ceftriaxone, and he recovered fully without any complication. . is a bacteria associated with plants; however, it can infect humans and vertebrate animals. The outcome seems favourable with the institution of appropriate antibiotics even in immunocompromised patients.
PubMed: 32313708
DOI: 10.1155/2020/7890305 -
Weather, Climate, and Society (Print) Jul 2023Climate change is expected to impact individuals' recreational choices, as changing temperatures and precipitation patterns influence participation in outdoor recreation...
Climate change is expected to impact individuals' recreational choices, as changing temperatures and precipitation patterns influence participation in outdoor recreation and alternative activities. This paper empirically investigates the relationship between weather and outdoor recreation using nationally representative data from the contiguous United States. We find that across most outdoor recreational activities, participation is lowest on the coldest days (<35 degrees Fahrenheit) and highest at moderately high temperatures (80 to 90 degrees). Notable exceptions to this trend include water sports and snow and ice sports, for which participation peaks at the highest and lowest temperatures, respectively. If individuals continue to respond to temperature changes the same way that they have in the recent past, in a future climate that has fewer cool days and more moderate and hot days, our model anticipates net participation across all outdoor recreation activities will increase by 88 million trips annually at 1 degree Celsius of warming (CONUS) and up to 401 million trips at 6 degrees of warming, valued between $3.2 billion and $15.6 billion in consumer surplus annually (2010 population). The increase in trips is driven by participation in water sports; excluding water sports from future projections decreases the consumer surplus gains by approximately 75 percent across all modeled degrees of warming. If individuals in northern regions respond to temperature like people in southern regions currently do (a proxy for adaptation), total outdoor recreation trips will increase by an additional 17 percent compared to no adaptation at 6 degrees of warming. This benefit is generally not seen at lower degrees of warming.
PubMed: 37415774
DOI: 10.1175/wcas-d-22-0060.1 -
Sensors (Basel, Switzerland) Apr 2023Temperature-controlled closed-loop systems are vital to the transportation of produce. By maintaining specific transportation temperatures and adjusting to environmental...
Temperature-controlled closed-loop systems are vital to the transportation of produce. By maintaining specific transportation temperatures and adjusting to environmental factors, these systems delay decomposition. Wireless sensor networks (WSN) can be used to monitor the temperature levels at different locations within these transportation containers and provide feedback to these systems. However, there are a range of unique challenges in WSN implementations, such as the cost of the hardware, implementation difficulties, and the general ruggedness of the environment. This paper presents the novel results of a real-life application, where a sensor network was implemented to monitor the environmental temperatures at different locations inside commercial temperature-controlled shipping containers. The possibility of predicting one or more locations inside the container in the absence or breakdown of a logger placed in that location is explored using combinatorial input-output settings. A total of 1016 machine learning (ML) models are exhaustively trained, tested, and validated in search of the best model and the best combinations to produce a higher prediction result. The statistical correlations between different loggers and logger combinations are studied to identify a systematic approach to finding the optimal setting and placement of loggers under a cost constraint. Our findings suggest that even under different and incrementally higher cost constraints, one can use empirical approaches such as neural networks to predict temperature variations in a location with an absent or failed logger, within a margin of error comparable to the manufacturer-specified sensor accuracy. In fact, the median test accuracy is 1.02 degrees Fahrenheit when using only a single sensor to predict the remaining locations under the assumptions of critical system failure, and drops to as little as 0.8 and 0.65 degrees Fahrenheit when using one or three more sensors in the prediction algorithm. We also demonstrate that, by using correlation coefficients and time series similarity measurements, one can identify the optimal input-output pairs for the prediction algorithm reliably under most instances. For example, discrete time warping can be used to select the best location to place the sensors with a 92% match between the lowest prediction error and the highest similarity sensor with the rest of the group. The findings of this research can be used for power management in sensor batteries, especially for long transportation routes, by alternating standby modes where the temperature data for the OFF sensors are predicted by the ON sensors.
PubMed: 37177507
DOI: 10.3390/s23094303