An insight into the epidemiology of foodborne zoonotic fascioliasis in small ruminants in northwestern region of Bangladesh.

Fascioliasis is one of the most common foodborne zoonotic infection of ruminants in Bangladesh. To estimate the prevalence and associated risk factors of fascioliasis and extent of liver damage, 825 livers of sheep and goats were randomly inspected during onsite slaughterhouse visiting in Naogaon, Natore, Rajshahi and Joypurhat districts. The overall prevalence of fascioliasis was 25.09% and significantly (P = 0.008) higher in goats (26.11%) than sheep (24.00%). During gross inspection, Fasciola infected livers were increased in size, fibrosed, fatty, multiple white or reddish necrotic foci on the parietal surface, hard to cut, calcified, and numerous mature and immature flukes were also observed. In histoarchitecture, inflammatory cell infiltration in the hepatic parenchyma and periportal area, fibrous connective tissue proliferation around necrotic area, hyperplastic bile duct, congestion, and primary biliary cirrhosis were seen in acute and chronic fascioliasis. Epidemiological investigations revealed that fascioliasis was higher in goats than sheep. Age, sex, BCS and season were found to have statistically significant associations with fascioliasis in goats. In case of sheep, age (OR = 5.8671; 95% CI: 2.9482-11.6757, P < 0.0001), sex (OR = 3.7317; 95% CI: 1.9052-7.3094, p < 0.0001), BCS (OR = 6.0346; 95% CI: 1.7986-20.2472, p < .0001), and season (OR = 8.2308; 95% CI: 3.9922-16.9693, p = < .0001) were also found to have statistically significant associations with fascioliasis. Results of the study can help for molecular epidemiology of fascioliasis in small ruminants to plan fluke control programs for safe food production.

Genomic landscape of NDM-1 producing multidrug-resistant Providencia stuartii causing burn wound infections in Bangladesh.

The increasing antimicrobial resistance in Providencia stuartii (P. stuartii) worldwide, particularly concerning for immunocompromised and burn patients, has raised concern in Bangladesh, where the significance of this infectious opportunistic pathogen had been previously overlooked, prompting a need for investigation. The two strains of P. stuartii (P. stuartii SHNIBPS63 and P. stuartii SHNIBPS71) isolated from wound swab of two critically injured burn patients were found to be multidrug-resistant and P. stuartii SHNIBPS63 showed resistance to all the 22 antibiotics tested as well as revealed the co-existence of blaVEB-6 (Class A), blaNDM-1 (Class B), blaOXA-10 (Class D) beta lactamase genes. Complete resistance to carbapenems through the production of NDM-1, is indicative of an alarming situation as carbapenems are considered to be the last line antibiotic to combat this pathogen. Both isolates displayed strong biofilm-forming abilities and exhibited resistance to copper, zinc, and iron, in addition to carrying multiple genes associated with metal resistance and the formation of biofilms. The study also encompassed a pangenome analysis utilizing a dataset of eighty-six publicly available P. stuartii genomes (n = 86), revealing evidence of an open or expanding pangenome for P. stuartii. Also, an extensive genome-wide analysis of all the P. stuartii genomes revealed a concerning global prevalence of diverse antimicrobial resistance genes, with a particular alarm raised over the abundance of carbapenem resistance gene blaNDM-1. Additionally, this study highlighted the notable genetic diversity within P. stuartii, significant informations about phylogenomic relationships and ancestry, as well as potential for cross-species transmission, raising important implications for public health and microbial adaptation across different environments.

Unveiling a high-risk epidemic clone (ST 357) of 'Difficult to Treat Extensively Drug-Resistant' (DT-XDR) Pseudomonas aeruginosa from a burn patient in Bangladesh: A resilient beast revealing coexistence of four classes of beta lactamases.

OBJECTIVES: Pseudomonas aeruginosa (P. aeruginosa) stands out as a key culprit in the colonization of burn wounds, instigating grave infections of heightened severity. In this study, we have performed comparative whole genome analysis of a difficult to treat extensively drug resistant P. aeruginosa isolated from a burn patient in order to elucidate genomic diversity, molecular patterns, mechanisms and genes responsible for conferring antimicrobial resistance and virulence. METHOD: P. aeruginosa SHNIBPS206 was isolated from an infected burn wound of a critically injured burn patient. Whole genome sequencing was carried out and annotated with Prokka. Sequence type, serotype, antimicrobial resistance genes and mechanisms, virulence genes, metal resistance genes and CRISPR/Cas systems were investigated. Later, pangenome analysis was carried out to find out genomic diversity. RESULT: P. aeruginosa SHNIBPS206 (MLST 357, Serotype O11) was resistant to 14 antibiotics including carbapenems and harboured all four classes of beta lactamase producing genes: Class A (blaPME-1, blaVEB-9), Class B (blaNDM-1), Class C (blaPDC-11) and Class D (blaOXA-846). Mutational analysis of Porin D gave valuable insights. Several efflux pump, virulence and metal resistance genes were also detected. Pangenome analysis revealed high genomic diversity among different strains of P. aeruginosa. CONCLUSION: To our knowledge, this is the first report of an extensively drug resistant ST 357 P. aeruginosa from Bangladesh, which is an epidemic high-risk P. aeruginosa clone. Further research and in-depth comprehensive studies are required to investigate the prevalence of such high-risk clone of P. aeruginosa in Bangladesh.

Spatio-temporal solutions of a diffusive directed dynamics model with harvesting.

The study considers a directed dynamics reaction-diffusion competition model to study the density of evolution for a single species population with harvesting effect in a heterogeneous environment, where all functions are spatially distributed in time series. The dispersal dynamics describe the growth of the species, which is distributed according to the resource function with no-flux boundary conditions. The analysis investigates the existence, positivity, persistence, and stability of solutions for both time-periodic and spatial functions. The carrying capacity and the distribution function are either arbitrary or proportional. It is observed that if harvesting exceeds the growth rate, then eventually, the population drops down to extinction. Several numerical examples are considered to support the theoretical results. Supplementary Information: The online version contains supplementary material available at 10.1007/s12190-022-01742-x.

Ethnic homophily affects vaccine prioritization strategies.

People are more likely to interact with other people of their ethnicity-a phenomenon known as ethnic homophily. In the United States, people of color are known to hold proportionately more high-contact jobs and are thus more at risk of virus infection. At the same time, these ethnic groups are on average younger than the rest of the population. This gives rise to interesting disease dynamics and non-trivial trade-offs that should be taken into consideration when developing prioritization strategies for future mass vaccine roll-outs. Here, we study the spread of COVID-19 through the US population, stratified by age, ethnicity, and occupation, using a detailed, previously-developed compartmental disease model. Based on historic data from the US mass COVID-19 vaccine roll-out that began in December 2020, we show, (i) how ethnic homophily affects the choice of optimal vaccine allocation strategy, (ii) that, notwithstanding potential ethical concerns, differentiating by ethnicity in these strategies can improve outcomes (e.g., fewer deaths), and (iii) that the most likely social context in the United States is very different from the standard assumptions made by models which do not account for ethnicity and this difference affects which allocation strategy is optimal. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Incorporating the mutational landscape of SARS-COV-2 variants and case-dependent vaccination rates into epidemic models.

Coronavirus Disease (COVID-19), which began as a small outbreak in Wuhan, China, in December 2019, became a global pandemic within months due to its high transmissibility. In the absence of pharmaceutical treatment, various non-pharmaceutical interventions (NPIs) to contain the spread of COVID-19 brought the entire world to a halt. After almost a year of seemingly returning to normalcy with the world's quickest vaccine development, the emergence of more infectious and vaccine resistant coronavirus variants is bringing the situation back to where it was a year ago. In the light of this new situation, we conducted a study to portray the possible scenarios based on the three key factors: impact of interventions (pharmaceutical and NPIs), vaccination rate, and vaccine efficacy. In our study, we assessed two of the most crucial factors, transmissibility and vaccination rate, in order to reduce the spreading of COVID-19 in a simple but effective manner. In order to incorporate the time-varying mutational landscape of COVID-19 variants, we estimated a weighted transmissibility composed of the proportion of existing strains that naturally vary over time. Additionally, we consider time varying vaccination rates based on the number of daily new cases. Our method for calculating the vaccination rate from past active cases is an effective approach in forecasting probable future scenarios as it actively tracks people's attitudes toward immunization as active case changes. Our simulations show that if a large number of individuals cannot be vaccinated by ensuring high efficacy in a short period of time, adopting NPIs is the best approach to manage disease transmission with the emergence of new vaccine breakthrough and more infectious variants.

Modeling the Impact of Climate Change on Cervid Chronic Wasting Disease in Semi-Arid South Texas.

Chronic wasting disease (CWD) is a spongiform encephalopathy disease caused by the transmission of infectious prion agents. CWD is a fatal disease that affects wild and farmed cervids in North America with few cases reported overseas. Social interaction of cervids, feeding practices by wildlife keepers and climate effects on the environmental carrying capacity all can affect CWD transmission in deer. Wildlife deer game hunting is economically important to the semi-arid South Texas region and is affected by climate change. In this paper, we model and investigate the effect of climate change on the spread of CWD using typical climate scenarios. We use a system of impulsive differential equations to depict the transmission of CWD between different age groups and gender of cervids. The carrying capacity and contact rates are assumed to depend on climate. Due to the polygamy of bucks, we use mating rates that depend on the number of bucks and does. We analyze the stability of the model and use simulations to study the effect of harvesting (culling) on eradicating the disease, given the climate of South Texas. We use typical climate change scenarios based on published data and our assumptions. For the climate indicator, we calculated and utilized the Standard Precipitation Evapotranspiration Index (SPEI). We found that climate change might hinder the efforts to reduce and effectively manage CWD as it becomes endemic to South Texas. The model shows the extinction of the deer population from this region is a likely outcome.

Evaluation of the United States COVID-19 vaccine allocation strategy.

BACKGROUND: Anticipating an initial shortage of vaccines for COVID-19, the Centers for Disease Control (CDC) in the United States developed priority vaccine allocations for specific demographic groups in the population. This study evaluates the performance of the CDC vaccine allocation strategy with respect to multiple potentially competing vaccination goals (minimizing mortality, cases, infections, and years of life lost (YLL)), under the same framework as the CDC allocation: four priority vaccination groups and population demographics stratified by age, comorbidities, occupation and living condition (congested or non-congested). METHODS AND FINDINGS: We developed a compartmental disease model that incorporates key elements of the current pandemic including age-varying susceptibility to infection, age-varying clinical fraction, an active case-count dependent social distancing level, and time-varying infectivity (accounting for the emergence of more infectious virus strains). The CDC allocation strategy is compared to all other possibly optimal allocations that stagger vaccine roll-out in up to four phases (17.5 million strategies). The CDC allocation strategy performed well in all vaccination goals but never optimally. Under the developed model, the CDC allocation deviated from the optimal allocations by small amounts, with 0.19% more deaths, 4.0% more cases, 4.07% more infections, and 0.97% higher YLL, than the respective optimal strategies. The CDC decision to not prioritize the vaccination of individuals under the age of 16 was optimal, as was the prioritization of health-care workers and other essential workers over non-essential workers. Finally, a higher prioritization of individuals with comorbidities in all age groups improved outcomes compared to the CDC allocation. CONCLUSION: The developed approach can be used to inform the design of future vaccine allocation strategies in the United States, or adapted for use by other countries seeking to optimize the effectiveness of their vaccine allocation strategies.

SARS-CoV-2 and Rohingya Refugee Camp, Bangladesh: Uncertainty and How the Government Took Over the Situation.

Background: Bangladesh hosts more than 800,000 Rohingya refugees from Myanmar. The low health immunity, lifestyle, access to good healthcare services, and social-security cause this population to be at risk of far more direct effects of COVID-19 than the host population. Therefore, evidence-based forecasting of the COVID-19 burden is vital in this regard. In this study, we aimed to forecast the COVID-19 obligation among the Rohingya refugees of Bangladesh to keep up with the disease outbreak's pace, health needs, and disaster preparedness. Methodology and Findings: To estimate the possible consequences of COVID-19 in the Rohingya camps of Bangladesh, we used a modified Susceptible-Exposed-Infectious-Recovered (SEIR) transmission model. All of the values of different parameters used in this model were from the Bangladesh Government's database and the relevant emerging literature. We addressed two different scenarios, i.e., the best-fitting model and the good-fitting model with unique consequences of COVID-19. Our best fitting model suggests that there will be reasonable control over the transmission of the COVID-19 disease. At the end of December 2020, there will be only 169 confirmed COVID-19 cases in the Rohingya refugee camps. The average basic reproduction number (R0) has been estimated to be 0.7563. Conclusions: Our analysis suggests that, due to the extensive precautions from the Bangladesh government and other humanitarian organizations, the coronavirus disease will be under control if the maintenance continues like this. However, detailed and pragmatic preparedness should be adopted for the worst scenario.

Integer Versus Fractional Order SEIR Deterministic and Stochastic Models of Measles.

In this paper, we compare the performance between systems of ordinary and (Caputo) fractional differential equations depicting the susceptible-exposed-infectious-recovered (SEIR) models of diseases. In order to understand the origins of both approaches as mean-field approximations of integer and fractional stochastic processes, we introduce the fractional differential equations (FDEs) as approximations of some type of fractional nonlinear birth and death processes. Then, we examine validity of the two approaches against empirical courses of epidemics; we fit both of them to case counts of three measles epidemics that occurred during the pre-vaccination era in three different locations. While ordinary differential equations (ODEs) are commonly used to model epidemics, FDEs are more flexible in fitting empirical data and theoretically offer improved model predictions. The question arises whether, in practice, the benefits of using FDEs over ODEs outweigh the added computational complexities. While important differences in transient dynamics were observed, the FDE only outperformed the ODE in one of out three data sets. In general, FDE modeling approaches may be worth it in situations with large refined data sets and good numerical algorithms.