Association between Global Monkeypox Cases and Meteorological Factors

Analysing monkeypox epidemic drivers: Policy simulation and multi-index modelling across 39 nations

Background

This study aimed to analyse the drivers of the monkeypox (Mpox) epidemic and policy simulation to support health care policies against the Mpox epidemic.

Methods

We established a three-round selection mechanism for 164 factors using Lasso and negative binomial regression to investigate the correlation between significant drivers and the cumulative confirmed cases of Mpox. Policy simulation for each driver was evaluated, and the varying effects of implementation at different times were examined.

Results

HIV/AIDS prevalence and air transport passengers carried were significant determinants of the risk of the Mpox epidemic across various countries, with regression coefficients of 1.417 and 0.766, respectively. A decrease in HIV/AIDS prevalence by 10, 20, 30, and 40% corresponded to reductions in the number of Mpox cases by 6.28, 6.55, 6.87, and 7.26%, respectively. Similarly, 20, 40, 60, and 80% travel restrictions led to reductions in Mpox cases by 7.16, 15.63, 26.28%, and 41.46%, respectively. Controlling air transport passengers carried in the first month could postpone outbreak onset by 0.5-2.0 months.

Conclusions

Mpox prevention and control policies should primarily focus on travel restrictions during high disease-risk periods and flight suspensions from high-risk nations in combination with regular HIV/AIDS prevention and treatment strategies.

Monkeypox virus (MPXV): A Brief account of global spread, epidemiology, virology, clinical features, pathogenesis, and therapeutic interventions

The largest monkeypox virus (MPXV) outbreak of the 21st century occurred in 2022, which caused epidemics in many countries. According to WHO, physical contact with infected persons, contaminated surfaces, or affected animals might be a source of this virus transmission. A febrile sickness including few symptoms found in MPX disease. Skin rash, lesions, fever, headache, fatigue, and muscle aches symptoms were observed commonly for this disease. Animal and in vitro, studies have shown that the antiviral medications cidofovir and brincidofovir are effective against MPXV. The first-generation vaccinia virus vaccine was developed in 1960, and it helped to protect against MPXV with its side effects. A second-generation vaccination with limitations was launched in 2000. However, the CDC advised vaccinations for risk groups in endemic countries, including positive patients and hospital employees. The JYNNEOS vaccine, administered in 2 doses, also provides protection from MPX. This article presents concisely the most recent findings regarding epidemiology, genomic transmission, signs and symptoms, pathogenesis, diagnosis, and therapeutic interventions for MPXV, which may be helpful to researchers and practitioners. WHO declared that MPX was no longer a global health emergency due to its declining case rate, and a number of countries have reported new incidences. Further research-based investigations must be carried out based on the 2022 outbreak.

A meta-meta-analysis of co-infection, secondary infections, and antimicrobial resistance in COVID-19 patients

The newly discovered coronavirus SARS-CoV-2 has sparked a worldwide pandemic of COVID-19, which has caused havoc on medical infrastructures, economies, and cultures around the world. Determining the whole scenario is essential since SARS-CoV-2 variants and sub-variants keep appearing after vaccinations and booster doses. The objective of this secondary meta-analysis is to analysis co-infection, secondary infections, and antimicrobial resistance (AMR) in COVID-19 patients. This study used five significant databases to conduct a systematic review and an overlap meta-analysis to evaluate the pooled estimates of co-infections and secondary infections. The summary of the meta-analysis showed an overall co-infection effect of 26.19% (95% confidence intervals CI: 21.39-31.01, I2 =98.78, n = 14 meta-analysis) among patients with COVID-19. A coinfection effect of 11.13% (95% CI: 9.7-12.56, I2 =99.14, n = 11 meta-analysis) for bacteria; 9.69% (95% CI: 1.21-7.90, I2 =98.33) for fungal and 3.48% (95% CI: 2.15-4.81, I2 =95.84) for viruses. A secondary infection effect of 19.03% (95% CI: 9.53-28.54, I2 =85.65) was pooled from 2 meta-analyses (Ave: 82 primary studies). This is the first study that compiles the results of all the previous three years meta-analyses into a single source and offers strong proof of co-infections and secondary infections in COVID-19 patients. Early detection of co-infection and AMR is crucial for COVID-19 patients in order to effective treatment.

Effects of fine particulate matter (PM2.5) and meteorological factors on the daily confirmed cases of COVID-19 in Bangkok during 2020-2021, Thailand

The ongoing global pandemic caused by the SARS-CoV-2 virus, known as COVID-19, has disrupted public health, businesses, and economies worldwide due to its widespread transmission. While previous research has suggested a possible link between environmental factors and increased COVID-19 cases, the evidence regarding this connection remains inconclusive. The purpose of this research is to determine whether or not there is a connection between the presence of fine particulate matter (PM2.5) and meteorological conditions and COVID-19 infection rates in Bangkok, Thailand. The study employs a statistical method called Generalized Additive Model (GAM) to find a positive and non-linear association between RH, AH, and R and the number of verified COVID-19 cases. The impacts of the seasons (especially summer) and rainfall on the trajectory of COVID-19 cases were also highlighted, with an adjusted R-square of 0.852 and a deviance explained of 85.60%, both of which were statistically significant (p < 0.05). The study results assist in preventing the future seasonal spread of COVID-19, and public health authorities may use these findings to make informed decisions and assess their policies.

Insight into vaccination and meteorological factors on daily COVID-19 cases and mortality in Bangladesh

The ongoing COVID-19 contagious disease caused by SARS-CoV-2 has disrupted global public health, businesses, and economies due to widespread infection, with 676.41 million confirmed cases and 6.77 million deaths in 231 countries as of February 07, 2023. To control the rapid spread of SARS-CoV-2, it is crucial to determine the potential determinants such as meteorological factors and their roles. This study examines how COVID-19 cases and deaths changed over time while assessing meteorological characteristics that could impact these disparities from the onset of the pandemic. We used data spanning two years across all eight administrative divisions, this is the first of its kind--showing a connection between meteorological conditions, vaccination, and COVID-19 incidences in Bangladesh. We further employed several techniques including Simple Exponential Smoothing (SES), Auto-Regressive Integrated Moving Average (ARIMA), Auto-Regressive Integrated Moving Average with explanatory variables (ARIMAX), and Automatic forecasting time-series model (Prophet). We further analyzed the effects of COVID-19 vaccination on daily cases and deaths. Data on COVID-19 cases collected include eight administrative divisions of Bangladesh spanning March 8, 2020, to January 31, 2023, from available online servers. The meteorological data include rainfall (mm), relative humidity (%), average temperature (°C), surface pressure (kPa), dew point (°C), and maximum wind speed (m/s). The observed wind speed and surface pressure show a significant negative impact on COVID-19 cases (-0.89, 95% confidence interval (CI): 1.62 to -0.21) and (-1.31, 95%CI: 2.32 to -0.29), respectively. Similarly, the observed wind speed and surface pressure show a significant negative impact on COVID-19 deaths (-0.87, 95% CI: 1.54 to -0.21) and (-3.11, 95%CI: 4.44 to -1.25), respectively. The impact of meteorological factors is almost similar when vaccination information is included in the model. However, the impact of vaccination in both cases and deaths model is significantly negative (for cases: 1.19, 95%CI: 2.35 to -0.38 and for deaths: 1.55, 95%CI: 2.88 to -0.43). Accordingly, vaccination effectively reduces the number of new COVID-19 cases and fatalities in Bangladesh. Thus, these results could assist future researchers and policymakers in the assessment of pandemics, by making thorough efforts that account for COVID-19 vaccinations and meteorological conditions.

Correlation of Dengue and Meteorological Factors in Bangladesh: A Public Health Concern

Dengue virus (DENV) is an enveloped, single-stranded RNA virus, a member of the Flaviviridae family (which causes Dengue fever), and an arthropod-transmitted human viral infection. Bangladesh is well known for having some of Asia's most vulnerable Dengue outbreaks, with climate change, its location, and it's dense population serving as the main contributors. For speculation about DENV outbreak characteristics, it is crucial to determine how meteorological factors correlate with the number of cases. This study used five time series models to observe the trend and forecast Dengue cases. Current data-based research has also applied four statistical models to test the relationship between Dengue-positive cases and meteorological parameters. Datasets were used from NASA for meteorological parameters, and daily DENV cases were obtained from the Directorate General of Health Service (DGHS) open-access websites. During the study period, the mean of DENV cases was 882.26 ± 3993.18, ranging between a minimum of 0 to a maximum of 52,636 daily confirmed cases. The Spearman's rank correlation coefficient between climatic variables and Dengue incidence indicated that no substantial relationship exists between daily Dengue cases and wind speed, temperature, and surface pressure (Spearman's rho; r = -0.007, p > 0.05; r = 0.085, p > 0.05; and r = -0.086, p > 0.05, respectively). Still, a significant relationship exists between daily Dengue cases and dew point, relative humidity, and rainfall (r = 0.158, p < 0.05; r = 0.175, p < 0.05; and r = 0.138, p < 0.05, respectively). Using the ARIMAX and GA models, the relationship for Dengue cases with wind speed is -666.50 [95% CI: -1711.86 to 378.86] and -953.05 [-2403.46 to 497.36], respectively. A similar negative relation between Dengue cases and wind speed was also determined in the GLM model (IRR = 0.98). Dew point and surface pressure also represented a negative correlation in both ARIMAX and GA models, respectively, but the GLM model showed a positive association. Additionally, temperature and relative humidity showed a positive correlation with Dengue cases (105.71 and 57.39, respectively, in the ARIMAX, 633.86, and 200.03 in the GA model). In contrast, both temperature and relative humidity showed negative relation with Dengue cases in the GLM model. In the Poisson regression model, windspeed has a substantial significant negative connection with Dengue cases in all seasons. Temperature and rainfall are significantly and positively associated with Dengue cases in all seasons. The association between meteorological factors and recent outbreak data is the first study where we are aware of the use of maximum time series models in Bangladesh. Taking comprehensive measures against DENV outbreaks in the future can be possible through these findings, which can help fellow researchers and policymakers.

Variant-specific deleterious mutations in the SARS-CoV-2 genome reveal immune responses and potentials for prophylactic vaccine development

Introduction: Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, has had a disastrous effect worldwide during the previous three years due to widespread infections with SARS-CoV-2 and its emerging variations. More than 674 million confirmed cases and over 6.7 million deaths have been attributed to successive waves of SARS-CoV-2 infections as of 29th January 2023. Similar to other RNA viruses, SARS-CoV-2 is more susceptible to genetic evolution and spontaneous mutations over time, resulting in the continual emergence of variants with distinct characteristics. Spontaneous mutations of SARS-CoV-2 variants increase its transmissibility, virulence, and disease severity and diminish the efficacy of therapeutics and vaccines, resulting in vaccine-breakthrough infections and re-infection, leading to high mortality and morbidity rates. Materials and methods: In this study, we evaluated 10,531 whole genome sequences of all reported variants globally through a computational approach to assess the spread and emergence of the mutations in the SARS-CoV-2 genome. The available data sources of NextCladeCLI 2.3.0 (https://clades.nextstrain.org/) and NextStrain (https://nextstrain.org/) were searched for tracking SARS-CoV-2 mutations, analysed using the PROVEAN, Polyphen-2, and Predict SNP mutational analysis tools and validated by Machine Learning models. Result: Compared to the Wuhan-Hu-1 reference strain NC 045512.2, genome-wide annotations showed 16,954 mutations in the SARS-CoV-2 genome. We determined that the Omicron variant had 6,307 mutations (retrieved sequence:1947), including 67.8% unique mutations, more than any other variant evaluated in this study. The spike protein of the Omicron variant harboured 876 mutations, including 443 deleterious mutations. Among these deleterious mutations, 187 were common and 256 were unique non-synonymous mutations. In contrast, after analysing 1,884 sequences of the Delta variant, we discovered 4,468 mutations, of which 66% were unique, and not previously reported in other variants. Mutations affecting spike proteins are mostly found in RBD regions for Omicron, whereas most of the Delta variant mutations drawn to focus on amino acid regions ranging from 911 to 924 in the context of epitope prediction (B cell & T cell) and mutational stability impact analysis protruding that Omicron is more transmissible. Discussion: The pathogenesis of the Omicron variant could be prevented if the deleterious and persistent unique immunosuppressive mutations can be targeted for vaccination or small-molecule inhibitor designing. Thus, our findings will help researchers monitor and track the continuously evolving nature of SARS-CoV-2 strains, the associated genetic variants, and their implications for developing effective control and prophylaxis strategies.

Mpox, Caused by the MPXV of the Clade IIb Lineage, Goes Global

Mpox is a great public health concern worldwide currently; thus, a global primary epidemiological analysis of mpox and a phylogenetic analysis of currently circulating MPXV strains based on open-source data is necessary. A total of 83,419 confirmed cases with 72 deaths were reported from 7 May to 23 December 2022, representing an ongoing increasing trend. Mpox was largely restricted to being endemic in children in West Africa (WA) before 2022, and it mainly spread from animals to humans. Our analysis highlights that mpox has not only spread across regions within Africa but has also led to most infection events outside Africa. Currently, mpox has been dominated by human-to-human spread in 110 countries, with the majority of cases distributed in the non-endemic regions of Europe and North America. These data indicate that the geographic range, transmission route, vulnerable populations, and clinical manifestations of mpox have changed, which suggests that the niche of mpox has the potential to change. Remarkably, approximately 38,025 suspected mpox cases were recorded in West and Central Africa during 1970-2022, which implied that the epidemiology of mpox in the two regions remained cryptic, suggesting that strengthening the accuracy of molecular diagnosis on this continent is a priority. Moreover, 617 mpox genomes have been obtained from 12 different hosts; these data imply that the high host diversity may contribute to its ongoing circulation and global outbreak. Furthermore, a phylogenetic analysis of 175 MPXV genome sequences from 38 countries (regions) showed that the current global mpox outbreak was caused by multiple sub-clades in the clade IIb lineage. These data suggest that MPXV strains from the clade IIb lineage may play a predominated role in the spread of mpox worldwide, implying that the current mpox outbreak has a single infection source. However, further investigations into the origin of the new global mpox outbreak are necessary. Therefore, our analysis highlights that adjusted timely interventive measures and surveillance programs, especially using cheap and quick strategies such as wastewater monitoring the DNA of MPXV in Africa (WA), are important for uncovering this disease's transmission source and chain, which will help curb its further spread.

A bibliometric study on Marburg virus research with prevention and control strategies

Marburg virus (MARV) is a pathogenic zoonotic RNA virus etiologic for Marburg virus disease (MVD), a severe hemorrhagic fever. This is a rare disease, with a high fatality rate, that spreads via infected blood or body fluids or indirectly via fomites (contaminated objects and substances such as clothed, beds, personal protective equipment, or medical equipments). A few vaccines to protect against MARV are undergoing clinical trials, but there is not yet an approved vaccine against this disease. Eventually, prevention and control guidelines should be adhered to rigorously to alleviate this infection. This bibliometric analysis aimed to harness narrative evaluation, emphasizing the significance of quantitative approaches and delineating the most thought-provoking concerns for researchers using VOSviewer software (Centre for Science and Technology Studies, Leiden University, the Netherlands). “Marburg Virus” OR “MARV” AND “Diseases” search criteria were used for the analysis of articles published between 1962 and 2022. Co-occurrence analysis was carried out, which characterized different thematic clusters. From this analysis, we found that 1688 published articles, and the number of publications increased across that period annually, with a growth rate of 8.78%. It is also conspicuous that the number of publications in the United States reached its acme during this period (i.e., 714 publications, accounting for 42.29% of the total), and the United States Army Medical Research Institute of Infectious Diseases published the most literature (i.e., 146 papers). Our study found that the three pre-eminent authors of Marburg virus papers were “FELDMANN, HEINZ“ of the National Institute of Allergy and Infectious Diseases, United States, “BECKER, STEPHAN” of the Philipps University of Marburg, Germany, and “GEISBERT, THOMAS W” of the University of Texas Medical Branch, United States. In this study we found that “JOURNAL OF VIROLOGY” has published the most pertinent literature, totaling 88 articles, followed by “The journal of Infectious Diseases”, which published 76 relevant papers, and “VIRUSES”, which published 52 corresponding papers. The most cited paper on the Marburg virus was published in Nature Medicine, with 522 total citations and 29 citations/year. Studies of the changing epidemiology and evolving nature of the virus and its ecological niche are required; breakthrough and implementation of the efficacious vaccine candidate(s), prophylaxis and therapeutic alternatives and supervision strategies, unveiling awareness-raising programs, and developing apposite and timely preparedness, prevention, and proactive control strategies are of utmost importance.

A hybrid forecasting technique for infection and death from the mpox virus

Objectives

The rising of new cases and death counts from the mpox virus (MPV) is alarming. In order to mitigate the impact of the MPV it is essential to have information of the virus's future position using more precise time series and stochastic models. In this present study, a hybrid forecasting system has been developed for new cases and death counts for MPV infection using the world daily cumulative confirmed and death series.

Methods

The original cumulative series was decomposed into new two subseries, such as a trend component and a stochastic series using the Hodrick-Prescott filter. To assess the efficacy of the proposed models, a comparative analysis with several widely recognized benchmark models, including auto-regressive (AR) model, auto-regressive moving average (ARMA) model, non-parametric auto-regressive (NPAR) model and artificial neural network (ANN), was performed.

Results

The introduction of two novel hybrid models, HPF 1 1 and HPF 3 4 , which demonstrated superior performance compared to all other models, as evidenced by their remarkable results in key performance indicators such as root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE), is a significant advancement in disease prediction.

Conclusion

The new models developed can be implemented in forecasting other diseases in the future. To address the current situation effectively, governments and stakeholders must implement significant changes to ensure strict adherence to standard operating procedures (SOPs) by the public. Given the anticipated continuation of increasing trends in the coming days, these measures are essential for mitigating the impact of the outbreak.