SpatialWavePredict: a tutorial-based primer and toolbox for forecasting growth trajectories using the ensemble spatial wave sub-epidemic modeling framework.

BACKGROUND: Dynamical mathematical models defined by a system of differential equations are typically not easily accessible to non-experts. However, forecasts based on these types of models can help gain insights into the mechanisms driving the process and may outcompete simpler phenomenological growth models. Here we introduce a friendly toolbox, SpatialWavePredict, to characterize and forecast the spatial wave sub-epidemic model, which captures diverse wave dynamics by aggregating multiple asynchronous growth processes and has outperformed simpler phenomenological growth models in short-term forecasts of various infectious diseases outbreaks including SARS, Ebola, and the early waves of the COVID-19 pandemic in the US. RESULTS: This tutorial-based primer introduces and illustrates a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using an ensemble spatial wave sub-epidemic model based on ordinary differential equations. Scientists, policymakers, and students can use the toolbox to conduct real-time short-term forecasts. The five-parameter epidemic wave model in the toolbox aggregates linked overlapping sub-epidemics and captures a rich spectrum of epidemic wave dynamics, including oscillatory wave behavior and plateaus. An ensemble strategy aims to improve forecasting performance by combining the resulting top-ranked models. The toolbox provides a tutorial for forecasting time-series trajectories, including the full uncertainty distribution derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score. CONCLUSIONS: We have developed the first comprehensive toolbox to characterize and forecast time-series data using an ensemble spatial wave sub-epidemic wave model. As an epidemic situation or contagion occurs, the tools presented in this tutorial can facilitate policymakers to guide the implementation of containment strategies and assess the impact of control interventions. We demonstrate the functionality of the toolbox with examples, including a tutorial video, and is illustrated using daily data on the COVID-19 pandemic in the USA.

Empowering Minds and Bodies: The Impact of Exercise on Multiple Sclerosis and Cognitive Health.

Multiple sclerosis (MS) is a global health concern affecting around 2.6 million people. It is characterised by neural inflammation, myelin breakdown and cognitive decline. Cognitive impairment, especially reduced cognitive processing speed (CPS), which affects up to 67% of MS patients and frequently manifests before mobility concerns, is one of the disease's most serious side effects. Effective adaptation and the application of cognitive rehabilitation treatments depend on the early diagnosis of cognitive impairment. Although pharmaceutical therapies have some drawbacks, endurance training has become a promising alternative. Intensity-controlled endurance exercise has the ability to delay the onset of MS symptoms and enhance cognitive function. Exercise has also been shown to have neuroprotective effects in a number of neurological disorders, including MS, Parkinson's disease and stroke. This includes both aerobic and resistance training. A mix of aerobic exercise and weight training has shown promise, especially for people with mild cognitive impairment, but according to recent studies any amount of physical activity is beneficial to cognitive performance. In conclusion, this in-depth analysis highlights the crucial part endurance exercise plays in treating MS-related cognitive impairment. It improves not only neurological health in general but also cognitive performance. Exercise can help control MS in a way that dramatically improves quality of life and well-being.

Gemigliptin, a potent selective dipeptidyl peptidase 4 inhibitor, protects endothelial progenitor cells by oxidative stress via caspase-3 dependent pathway.

Endothelial progenitor cells (EPCs) are exclusive players in vasculogenesis and endothelial regeneration. EPCs are of two types and their differentiation is mediated by different growth factors. A decrease in EPC number and function causes cardiovascular abnormalities and reduced angiogenesis. Various studies has documented a role of EPCs in diabetes. EPCs treatment with different drugs improve insulin secretion but causes other abnormalities. In vivo and in vitro studies have reported anti glycation effect of gemigliptin but no data is available on in vitro effect of gemigliptin on EPC number and functional credibility. The current study was aimed to find an in vitro effect of gemigliptin on EPC number and function along with an effective treatment dose of gemigliptin. EPCs were isolated, cultured and phenotypically characterized using Dil- AcLDL and ulex-lectin fluorescence staining. EPCs were then treated with different doses of Zemiglo and their viability analyzed with viability assay using water-soluble tetrazolium salt (WST-1), by Annexin V and Propidium Iodide (PI) staining, senescence-associated beta-galactosidase (SA-ß-gal) staining, western blot and Flow cytometric analysis of apoptotic signals. The results demonstrated that the isolated EPCs has typical endothelial phenotypes. And these EPCs were of two types based on morphology i.e., early and late EPCs. Gemigliptin dose dependently improved the EPCs morphology and increased EPCs viability, the most effective dose being the 20 µM. Gemigliptin at 10 µM, 20 µM and 50 µM significantly increased the BCL-2 levels and at 20 µM significantly decreased the Caspase-3 levels in EPCs. In conclusion, gemigliptin dose dependently effects the EPCs viability and morphology through Caspase-3 signaling. Our results are the first report of gemigliptin effect on EPC viability and morphology.

SubEpiPredict: A tutorial-based primer and toolbox for fitting and forecasting growth trajectories using the ensemble n-sub-epidemic modeling framework.

An ensemble n-sub-epidemic modeling framework that integrates sub-epidemics to capture complex temporal dynamics has demonstrated powerful forecasting capability in previous works. This modeling framework can characterize complex epidemic patterns, including plateaus, epidemic resurgences, and epidemic waves characterized by multiple peaks of different sizes. In this tutorial paper, we introduce and illustrate SubEpiPredict, a user-friendly MATLAB toolbox for fitting and forecasting time series data using an ensemble n-sub-epidemic modeling framework. The toolbox can be used for model fitting, forecasting, and evaluation of model performance of the calibration and forecasting periods using metrics such as the weighted interval score (WIS). We also provide a detailed description of these methods including the concept of the n-sub-epidemic model, constructing ensemble forecasts from the top-ranking models, etc. For the illustration of the toolbox, we utilize publicly available daily COVID-19 death data at the national level for the United States. The MATLAB toolbox introduced in this paper can be very useful for a wider group of audiences, including policymakers, and can be easily utilized by those without extensive coding and modeling backgrounds.

GrowthPredict: A toolbox and tutorial-based primer for fitting and forecasting growth trajectories using phenomenological growth models.

Simple dynamic modeling tools can help generate real-time short-term forecasts with quantified uncertainty of the trajectory of diverse growth processes unfolding in nature and society, including disease outbreaks. An easy-to-use and flexible toolbox for this purpose is lacking. This tutorial-based primer introduces and illustrates GrowthPredict, a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using phenomenological dynamic growth models based on ordinary differential equations. This toolbox is accessible to a broad audience, including students training in mathematical biology, applied statistics, and infectious disease modeling, as well as researchers and policymakers who need to conduct short-term forecasts in real-time. The models included in the toolbox capture exponential and sub-exponential growth patterns that typically follow a rising pattern followed by a decline phase, a common feature of contagion processes. Models include the 1-parameter exponential growth model and the 2-parameter generalized-growth model, which have proven useful in characterizing and forecasting the ascending phase of epidemic outbreaks. It also includes the 2-parameter Gompertz model, the 3-parameter generalized logistic-growth model, and the 3-parameter Richards model, which have demonstrated competitive performance in forecasting single peak outbreaks. We provide detailed guidance on forecasting time-series trajectories and available software ( https://github.com/gchowell/forecasting_growthmodels ), including the full uncertainty distribution derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance across different models, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score. This tutorial and toolbox can be broadly applied to characterizing and forecasting time-series data using simple phenomenological growth models. As a contagion process takes off, the tools presented in this tutorial can help create forecasts to guide policy regarding implementing control strategies and assess the impact of interventions. The toolbox functionality is demonstrated through various examples, including a tutorial video, and the examples use publicly available data on the monkeypox (mpox) epidemic in the USA.

Endothelial Progenitor Cells: A Brief Update.

An enormous amount of current data has suggested involvement of endothelial progenitor cells (EPCs) in neovasculogenesis in both human and animal models. EPC level is an indicator of possible cardiovascular risk such as Alzheimer disease. EPC therapeutics requires its identification, isolation, differentiation and thus expansion. We approach here the peculiar techniques through current and previous reports available to find the most plausible and fast way of their expansion to be used in therapeutics. We discuss here the techniques for EPCs isolation from different resources like bone marrow and peripheral blood circulation. EPCs have been isolated by methods which used fibronectin plating and addition of various growth factors to culture media. Particularly, the investigations which tried to enhance EPC differentiation while inducing with growth factors and endothelial nitric oxide synthase are shared. We also include the cryopreservation and other storage methods of EPCs for a longer time. Sufficient amount of EPCs are required in transplantation and other therapeutics which signifies their in vitro expansion. We highlight the role of EPCs in transplantation which improved neurogenesis in animal models of ischemic stroke and human with acute cerebral infarct in the brain. Accumulatively, these data suggest the exhilarating route for enhancing EPC number to make their use in the clinic. Finally, we identify the expression of specific biomarkers in EPCs under the influence of growth factors. This review provides a brief overview of factors involved in EPC expansion and transplantation and raises interesting questions at every stage with constructive suggestions.

Structural identifiability analysis of epidemic models based on differential equations: a tutorial-based primer.

The successful application of epidemic models hinges on our ability to estimate model parameters from limited observations reliably. An often-overlooked step before estimating model parameters consists of ensuring that the model parameters are structurally identifiable from the observed states of the system. In this tutorial-based primer, intended for a diverse audience, including students training in dynamic systems, we review and provide detailed guidance for conducting structural identifiability analysis of differential equation epidemic models based on a differential algebra approach using differential algebra for identifiability of systems (DAISY) and Mathematica (Wolfram Research). This approach aims to uncover any existing parameter correlations that preclude their estimation from the observed variables. We demonstrate this approach through examples, including tutorial videos of compartmental epidemic models previously employed to study transmission dynamics and control. We show that the lack of structural identifiability may be remedied by incorporating additional observations from different model states, assuming that the system's initial conditions are known, using prior information to fix some parameters involved in parameter correlations, or modifying the model based on existing parameter correlations. We also underscore how the results of structural identifiability analysis can help enrich compartmental diagrams of differential-equation models by indicating the observed state variables and the results of the structural identifiability analysis.

Factors Associated with Chikungunya Infection among Pregnant Women in Grenada, West Indies.

Neonates are vulnerable to vector-borne diseases given the potential for mother-to-child congenital transmission. To determine factors associated with chikungunya virus (CHIKV) infection among pregnant women in Grenada, West Indies, a retrospective cohort study enrolled women who were pregnant during the 2014 CHIKV epidemic. In all, 520/688 women (75.5%) were CHIKV IgG positive. Low incomes, use of pit latrines, lack of home window screens, and subjective reporting of frequent mosquito bites were associated with increased risk of CHIKV infection in bivariate analyses. In the multivariate modified Poisson regression model, low income (adjusted relative risk [aRR]: 1.05 [95% CI: 1.01-1.10]) and frequent mosquito bites (aRR: 1.05 [95% CI: 1.01-1.10]) were linked to increased infection risk. In Grenada, markers of low socioeconomic status are associated with CHIKV infection among pregnant women. Given that Grenada will continue to face vector-borne outbreaks, interventions dedicated to improving living conditions of the most disadvantaged will help reduce the incidence of arboviral infections.

A MATLAB toolbox to fit and forecast growth trajectories using phenomenological growth models: Application to epidemic outbreaks.

Background: Simple dynamic modeling tools can be useful for generating real-time short-term forecasts with quantified uncertainty of the trajectory of diverse growth processes unfolding in nature and society, including disease outbreaks. An easy-to-use and flexible toolbox for this purpose is lacking. Results: In this tutorial-based primer, we introduce and illustrate a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using phenomenological dynamic growth models based on ordinary differential equations. This toolbox is accessible to various audiences, including students training in time-series forecasting, dynamic growth modeling, parameter estimation, parameter uncertainty and identifiability, model comparison, performance metrics, and forecast evaluation, as well as researchers and policymakers who need to conduct short-term forecasts in real-time. The models included in the toolbox capture exponential and sub-exponential growth patterns that typically follow a rising pattern followed by a decline phase, a common feature of contagion processes. Models include the 2-parameter generalized-growth model, which has proved useful to characterize and forecast the ascending phase of epidemic outbreaks, and the Gompertz model as well as the 3-parameter generalized logistic-growth model and the Richards model, which have demonstrated competitive performance in forecasting single peak outbreaks.The toolbox provides a tutorial for forecasting time-series trajectories that include the full uncertainty distribution, derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance across different models, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score. Conclusions: We have developed the first comprehensive toolbox to characterize and forecast time-series data using simple phenomenological growth models. As a contagion process takes off, the tools presented in this tutorial can facilitate policymaking to guide the implementation of control strategies and assess the impact of interventions. The toolbox functionality is demonstrated through various examples, including a tutorial video, and is illustrated using weekly data on the monkeypox epidemic in the USA.

An ensemble n-sub-epidemic modeling framework for short-term forecasting epidemic trajectories: Application to the COVID-19 pandemic in the USA.

We analyze an ensemble of n-sub-epidemic modeling for forecasting the trajectory of epidemics and pandemics. These ensemble modeling approaches, and models that integrate sub-epidemics to capture complex temporal dynamics, have demonstrated powerful forecasting capability. This modeling framework can characterize complex epidemic patterns, including plateaus, epidemic resurgences, and epidemic waves characterized by multiple peaks of different sizes. We systematically assess their calibration and short-term forecasting performance in short-term forecasts for the COVID-19 pandemic in the USA from late April 2020 to late February 2022. We compare their performance with two commonly used statistical ARIMA models. The best fit sub-epidemic model and three ensemble models constructed using the top-ranking sub-epidemic models consistently outperformed the ARIMA models in terms of the weighted interval score (WIS) and the coverage of the 95% prediction interval across the 10-, 20-, and 30-day short-term forecasts. In our 30-day forecasts, the average WIS ranged from 377.6 to 421.3 for the sub-epidemic models, whereas it ranged from 439.29 to 767.05 for the ARIMA models. Across 98 short-term forecasts, the ensemble model incorporating the top four ranking sub-epidemic models (Ensemble(4)) outperformed the (log) ARIMA model 66.3% of the time, and the ARIMA model, 69.4% of the time in 30-day ahead forecasts in terms of the WIS. Ensemble(4) consistently yielded the best performance in terms of the metrics that account for the uncertainty of the predictions. This framework can be readily applied to investigate the spread of epidemics and pandemics beyond COVID-19, as well as other dynamic growth processes found in nature and society that would benefit from short-term predictions.

An ensemble n -sub-epidemic modeling framework for short-term forecasting epidemic trajectories: Application to the COVID-19 pandemic in the USA.

We analyze an ensemble of n -sub-epidemic modeling for forecasting the trajectory of epidemics and pandemics. These ensemble modeling approaches, and models that integrate sub-epidemics to capture complex temporal dynamics, have demonstrated powerful forecasting capability. This modeling framework can characterize complex epidemic patterns, including plateaus, epidemic resurgences, and epidemic waves characterized by multiple peaks of different sizes. We systematically assess their calibration and short-term forecasting performance in short-term forecasts for the COVID-19 pandemic in the USA from late April 2020 to late February 2022. We compare their performance with two commonly used statistical ARIMA models. The best fit sub-epidemic model and three ensemble models constructed using the top-ranking sub-epidemic models consistently outperformed the ARIMA models in terms of the weighted interval score (WIS) and the coverage of the 95% prediction interval across the 10-, 20-, and 30-day short-term forecasts. In the 30-day forecasts, the average WIS ranged from 377.6 to 421.3 for the sub-epidemic models, whereas it ranged from 439.29 to 767.05 for the ARIMA models. Across 98 short-term forecasts, the ensemble model incorporating the top four ranking sub-epidemic models (Ensemble(4)) outperformed the (log) ARIMA model 66.3% of the time, and the ARIMA model 69.4% of the time in 30-day ahead forecasts in terms of the WIS. Ensemble(4) consistently yielded the best performance in terms of the metrics that account for the uncertainty of the predictions. This framework could be readily applied to investigate the spread of epidemics and pandemics beyond COVID-19, as well as other dynamic growth processes found in nature and society that would benefit from short-term predictions. Summary: The COVID-19 pandemic has highlighted the urgent need to develop reliable tools to forecast the trajectory of epidemics and pandemics in near real-time. We describe and apply an ensemble n -sub-epidemic modeling framework for forecasting the trajectory of epidemics and pandemics. We systematically assess its calibration and short-term forecasting performance in weekly 10-30 days ahead forecasts for the COVID-19 pandemic in the USA from late April 2020 to late February 2022 and compare its performance with two different statistical ARIMA models. This framework demonstrated reliable forecasting performance and substantially outcompeted the ARIMA models. The forecasting performance was consistently best for the ensemble sub-epidemic models incorporating a higher number of top-ranking sub-epidemic models. The ensemble model incorporating the top four ranking sub-epidemic models consistently yielded the best performance, particularly in terms of the coverage rate of the 95% prediction interval and the weighted interval score. This framework can be applied to forecast other growth processes found in nature and society including the spread of information through social media.

Infection fatality rate and infection attack rate of COVID-19 in South American countries.

BACKGROUND: The ongoing COVID-19 pandemic hit South America badly with multiple waves. Different COVID-19 variants have been storming across the region, leading to more severe infections and deaths even in places with high vaccination coverage. This study aims to assess the spatiotemporal variability of the COVID-19 pandemic and estimate the infection fatality rate (IFR), infection attack rate (IAR) and reproduction number ([Formula: see text]) for twelve most affected South American countries. METHODS: We fit a susceptible-exposed-infectious-recovered (SEIR)-based model with a time-varying transmission rate to the reported COVID-19 deaths for the twelve South American countries with the highest mortalities. Most of the epidemiological datasets analysed in this work are retrieved from the disease surveillance systems by the World Health Organization, Johns Hopkins Coronavirus Resource Center and Our World in Data. We investigate the COVID-19 mortalities in these countries, which could represent the situation  for the overall South American region. We employ COVID-19 dynamic model with-and-without vaccination considering time-varying flexible transmission rate to estimate IFR, IAR and [Formula: see text] of COVID-19 for the South American countries. RESULTS: We simulate the model in each scenario under suitable parameter settings and yield biologically reasonable estimates for IFR (varies between 0.303% and 0.723%), IAR (varies between 0.03 and 0.784) and [Formula: see text] (varies between 0.7 and 2.5) for the 12 South American countries. We observe that the severity, dynamical patterns of deaths and time-varying transmission rates among the countries are highly heterogeneous. Further analysis of the model with the effect of vaccination highlights that increasing the vaccination rate could help suppress the pandemic in South America. CONCLUSIONS: This study reveals possible reasons for the two waves of COVID-19 outbreaks in South America. We observed reductions in the transmission rate corresponding to each wave plausibly due to improvement in nonpharmaceutical interventions measures and human protective behavioral reaction to recent deaths. Thus, strategies coupling social distancing and vaccination could substantially suppress the mortality rate of COVID-19 in South America.

An investigation of spatial-temporal patterns and predictions of the coronavirus 2019 pandemic in Colombia, 2020-2021.

Colombia announced the first case of severe acute respiratory syndrome coronavirus 2 on March 6, 2020. Since then, the country has reported a total of 5,002,387 cases and 127,258 deaths as of October 31, 2021. The aggressive transmission dynamics of SARS-CoV-2 motivate an investigation of COVID-19 at the national and regional levels in Colombia. We utilize the case incidence and mortality data to estimate the transmission potential and generate short-term forecasts of the COVID-19 pandemic to inform the public health policies using previously validated mathematical models. The analysis is augmented by the examination of geographic heterogeneity of COVID-19 at the departmental level along with the investigation of mobility and social media trends. Overall, the national and regional reproduction numbers show sustained disease transmission during the early phase of the pandemic, exhibiting sub-exponential growth dynamics. Whereas the most recent estimates of reproduction number indicate disease containment, with Rt<1.0 as of October 31, 2021. On the forecasting front, the sub-epidemic model performs best at capturing the 30-day ahead COVID-19 trajectory compared to the Richards and generalized logistic growth model. Nevertheless, the spatial variability in the incidence rate patterns across different departments can be grouped into four distinct clusters. As the case incidence surged in July 2020, an increase in mobility patterns was also observed. On the contrary, a spike in the number of tweets indicating the stay-at-home orders was observed in November 2020 when the case incidence had already plateaued, indicating the pandemic fatigue in the country.

Knowledge, attitude, and practice of artificial intelligence among doctors and medical students in Pakistan: A cross-sectional online survey.

Background: The use of Artificial intelligence (AI) has gained popularity during the last few decades and its use in medicine is increasing globally. Developing countries like Pakistan are lagging in the implementation of AI-based solutions in healthcare. There is a need to incorporate AI in the health system which may help not only in expediting diagnosis and management but also injudicious resource allocation. Objective: To determine the knowledge, attitude, and practice of AI among doctors and medical students in Pakistan. Materials and methods: We conducted a cross-sectional study using an online questionnaire-based survey regarding demographic details, knowledge, perception, and practice of AI. A sample of 470 individuals including doctors and medical students were selected using the convenient sampling technique. The chi-square test was applied for the comparison of variables. Results: Out of 470 individuals, 223(47.45%) were doctors and 247(52.55%) were medical students. Among these, 165(74%) doctors and 170(68.8%) medical students had a basic knowledge of AI but only 61(27.3%) doctors and 48(19.4%) students were aware of its medical applications. Regarding attitude, 237(76.7%) individuals supported AI's inclusion in curriculum, 368(78.3%) and 305(64.9%), 281(59.8%) and 269(57.2%) acknowledged its necessity in radiology, pathology, and COVID-19 pandemic respectively. Conclusion: The majority of doctors and medical students lack knowledge about AI and its applications, but had a positive view of AI in the field of medicine and were willing to adopt it.

Ameliorative effect of Spatoglossum asperum and its solvent fractions against acetaminophen-induced liver dysfunction in rats.

Acetaminophen (APAP) is a widely consumed drug for pain management and treatment of pyrexia. However, beyond its recommended dose, it becomes harmful for health and may induce acute liver dysfunction. Current work is designed to ameliorate the APAP induced liver toxicity which was induced in rats by giving intra-peritoneal injection of APAP (800mg/kg) dissolved in 40% polyethylene glycol at day 1 and day 14. APAP dosed/intoxicated rats orally administered either with ethanol extract of Spatoglossum asperum (200mg/kg) and its fractions including n-hexane, chloroform and methanol soluble in a dose of 150mg/kg each daily for 14 days in their respective groups. APAP dosed rats showed remarkable elevation in hepatic biomarkers viz., alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, lactate dehydrogenases, total bilirubin, pro-inflammatory cytokines interleukine-6 and apoptotic protein (caspase-3). In addition, hepatic oxidative stress (lipid per oxidation and indirect nitric oxide) and antioxidant biomarkers (glutathione peroxidase, catalase and reduced glutathione) were also altered. Whereas APAP dosed rats treated with ethanol extract of S. asperum and its fractions showed amelioration in concentration of hepatic enzymes, pro-inflammatory cytokines, apoptotic protein and reduction in hepatic oxidative stress by decreasing the lipid peroxidation, indirect nitric oxide and uplifting the activities of antioxidant enzymes and protein.

Transmission dynamics and forecasts of the COVID-19 pandemic in Mexico, March-December 2020.

Mexico has experienced one of the highest COVID-19 mortality rates in the world. A delayed implementation of social distancing interventions in late March 2020 and a phased reopening of the country in June 2020 has facilitated sustained disease transmission in the region. In this study we systematically generate and compare 30-day ahead forecasts using previously validated growth models based on mortality trends from the Institute for Health Metrics and Evaluation for Mexico and Mexico City in near real-time. Moreover, we estimate reproduction numbers for SARS-CoV-2 based on the methods that rely on genomic data as well as case incidence data. Subsequently, functional data analysis techniques are utilized to analyze the shapes of COVID-19 growth rate curves at the state level to characterize the spatiotemporal transmission patterns of SARS-CoV-2. The early estimates of the reproduction number for Mexico were estimated between Rt ~1.1-1.3 from the genomic and case incidence data. Moreover, the mean estimate of Rt has fluctuated around ~1.0 from late July till end of September 2020. The spatial analysis characterizes the state-level dynamics of COVID-19 into four groups with distinct epidemic trajectories based on epidemic growth rates. Our results show that the sequential mortality forecasts from the GLM and Richards model predict a downward trend in the number of deaths for all thirteen forecast periods for Mexico and Mexico City. However, the sub-epidemic and IHME models perform better predicting a more realistic stable trajectory of COVID-19 mortality trends for the last three forecast periods (09/21-10/21, 09/28-10/27, 09/28-10/27) for Mexico and Mexico City. Our findings indicate that phenomenological models are useful tools for short-term epidemic forecasting albeit forecasts need to be interpreted with caution given the dynamic implementation and lifting of social distancing measures.

Evaluation of the High Resolution Melting Approach for Detection of ß-Thalassemia Gene Mutations.

ß-Thalassemia (ß-thal), an autosomal recessive hemoglobinopathy, is one of the most common genetic disorders in Pakistan. Awareness of this disease, genetic counseling, extended family carrier screening and prenatal diagnosis (PND) are helpful in prevention and control. Currently, direct DNA sequencing and multiple amplification refractory mutation system-polymerase chain reaction (MARMS-PCR) are the methods used to detect ß-thal mutations, the latter being the most widely used. This study aimed to evaluate PCR-high resolution melting (PCR-HRM) analysis for the detection of most common ß-thal mutations that are found in Pakistan. This study was designed to identify the ß-thal mutations using PCR-HRM analysis in a total of 90 samples [blood and chorionic villus sampling (CVS)]. These samples were first screened for routine mutations by MARMS-PCR and then evaluated by PCR-HRM analysis. The results of PCR-HRM analyses were further confirmed by direct DNA sequencing and all analyses interpreted the same results in all 90 samples. Eleven cases (36.6%) were detected to carry IVS-I-5 (G>C) (HBB: c0.92 + 5G>C), six cases (20.0%) with frameshift codons (FSC) 41/42 (-TTCT) (HBB: c.126_129delCTTT), five cases (16.0%) were diagnosed with codon 15 (G>A) (HBB: c.47G>A), three cases (10.0%) were found with codon 30 (G>C) (HBB: c.93G>C), one case was diagnosed with FSC 16 (-C) (HBB: c.51delC), one with IVS-I-1 (G>T) (HBB: c0.92 + 1G>T) and one with codon 5 (-CT) (HBB: c.17_18delCT). The PCR-HRM analysis represents a less tedious and more useful method for the detection of ß-globin gene mutations.