A Comparative Analysis of COVID Forecasting by Using Various Machine Learning Methods

Covid-19 emerged as one of the most infectious diseases in the history of mankind, affecting nearly 250 million people all over the world in just a short period. The pandemic which started in China, has now spread all over the world, taking about 5 million lives globally. This has also severely affected the economies of countries and has proved to be a burden on health care systems. Due to these reasons, forecasting the spread of the disease has become critical so that concerned government authorities in countries can have the chance to mitigate the spread and plan health care resources efficiently and properly. This makes it more important to have a reliable forecast so that resources can be planned ahead of time. In the present work, linear regression is used for time forecasting the spread of Covid-19 in Pakistan. Statistical parameters and metrics have been used to evaluate and validate the model. The results show that linear regression results are highly reliable, time efficient and accurate. &nbsp

Estimation of COVID-19 spread curves integrating global data and borrowing information

Currently, novel coronavirus disease 2019 (COVID-19) is a big threat to global health. The rapid spread of the virus has created pandemic, and countries all over the world are struggling with a surge in COVID-19 infected cases. There are no drugs or other therapeutics approved by the US Food and Drug Administration to prevent or treat COVID-19: information on the disease is very limited and scattered even if it exists. This motivates the use of data integration, combining data from diverse sources and eliciting useful information with a unified view of them. In this paper, we propose a Bayesian hierarchical model that integrates global data for real-time prediction of infection trajectory for multiple countries. Because the proposed model takes advantage of borrowing information across multiple countries, it outperforms an existing individual country-based model. As fully Bayesian way has been adopted, the model provides a powerful predictive tool endowed with uncertainty quantification. Additionally, a joint variable selection technique has been integrated into the proposed modeling scheme, which aimed to identify possible country-level risk factors for severe disease due to COVID-19

Audit of Antenatal Testing of Sexually Transmissible Infections and Blood Borne Viruses at Western Australian Hospitals

In August 2007, the Western Australian Department of Health (DOH) released updated recommendations for testing of sexually transmissible infections (STI) and blood-borne viruses (BBV) in antenates. Prior to this, the Royal Australian & New Zealand College of Obstetricians & Gynaecologists (RANZCOG) antenatal testing recommendations had been accepted practice in most antenatal settings. The RANZCOG recommends that testing for HIV, syphilis, hepatitis B and C be offered at the first antenatal visit. The DOH recommends that in addition, chlamydia testing be offered. We conducted a baseline audit of antenatal STI/BBV testing in women who delivered at selected public hospitals before the DOH recommendations. We examined the medical records of 200 women who had delivered before 1st July 2007 from each of the sevenWAhospitals included in the audit. STI and BBV testing information and demographic data were collected. Of the 1,409 women included, 1,205 (86%) were non-Aboriginal and 200 (14%) were Aboriginal. High proportions of women had been tested for HIV (76%), syphilis (86%), hepatitis C (87%) and hepatitis B (88%). Overall, 72% of women had undergone STI/BBV testing in accordance with RANZCOG recommendations. However, chlamydia testing was evident in only 18% of records. STI/BBV prevalence ranged from 3.9% (CI 1.5– 6.3%) for chlamydia, to 1.7% (CI 1–2.4%) for hepatitis C, 0.7% (CI 0.3–1.2) for hepatitis B and 0.6% (CI 0.2–1) for syphilis. Prior to the DOH recommendations, nearly three-quarters of antenates had undergone STI/BBV testing in accordance with RANZCOG recommendations, but less than one fifth had been tested for chlamydia. The DOH recommendations will be further promoted with the assistance of hospitals and other stakeholders. A future audit will be conducted to determine the proportion of women tested according to the DOH recommendations. The hand book from this conference is available for download Published in 2008 by the Australasian Society for HIV Medicine Inc © Australasian Society for HIV Medicine Inc 2008 ISBN: 978-1-920773-59-

Comparison of exponential smoothing and ARIMA time series models for forecasting COVID-19 cases: a secondary data analysis

Background: In order to manage outbreaks and plan resources, health systems must be capable of accurately projecting COVID-19 case patterns. Health systems can effectively predict future illness patterns by using mathematical and statistical modelling of infectious diseases. Different methods have been used with comparatively good accuracy for various prediction goals in medical sciences. Some illustrations are provided by statistical techniques intended to forecast epidemic cases. In order to increase healthcare systems readiness, this study aimed to identify the most accurate models for COVID-19 with a high global prevalence of positive cases. Methods: Exponential smoothing model and ARIMA were employed on time series datasets to forecast confirmed cases in upcoming months and hence the effectiveness of these predictive models were compared on the basis of performance measures. Results: It was seen that the ARIMA (0,0,2) model is best fitted with smaller values of performance measures (RMSE=4.46 and MAE=2.86) while employed on the recent dataset for short duration. Holt-Winters Exponential smoothing model was found to be more accurate to deal with a longer period of time series based data. Conclusions: The study revealed that working with recent dataset is more accurate to forecast the number of confirmed cases as compared to the data collected for longer period. The early-stage warnings through these predictive models would be beneficial for governments and health professionals to be prepared with the strategies at different levels for public health prevention

Measures for diagnosing and treating infections by a novel coronavirus responsible for a pneumonia outbreak originating in Wuhan, China.

On 10 January 2020, a new coronavirus causing a pneumonia outbreak in Wuhan City in central China was denoted as 2019-nCoV by the World Health Organization (WHO). As of 24 January 2020, there were 887 confirmed cases of 2019-nCoV infection, including 26 deaths, reported in China and other countries. Therefore, combating this new virus and stopping the epidemic is a matter of urgency. Here, we focus on advances in research and development of fast diagnosis methods, as well as potential prophylactics and therapeutics to prevent or treat 2019-nCoV infection

Drivers for Rift Valley fever emergence in Mayotte: A Bayesian modelling approach

Rift Valley fever (RVF) is a major zoonotic and arboviral hemorrhagic fever. The conditions leading to RVF epidemics are still unclear, and the relative role of climatic and anthropogenic factors may vary between ecosystems. Here, we estimate the most likely scenario that led to RVF emergence on the island of Mayotte, following the 2006–2007 African epidemic. We developed the first mathematical model for RVF that accounts for climate, animal imports and livestock susceptibility, which is fitted to a 12-years dataset. RVF emergence was found to be triggered by the import of infectious animals, whilst transmissibility was approximated as a linear or exponential function of vegetation density. Model forecasts indicated a very low probability of virus endemicity in 2017, and therefore of re-emergence in a closed system (i.e. without import of infected animals). However, the very high proportion of naive animals reached in 2016 implies that the island remains vulnerable to the import of infectious animals. We recommend reinforcing surveillance in livestock, should RVF be reported is neighbouring territories. Our model should be tested elsewhere, with ecosystem-specific data

Evidence and information for national injection safety policies

The adverse consequences of poor injection practices have been reported for a few decades. However, key elements of evidence and information were lacking to allow decision-makers to formulate policies for the safe and appropriate use of injections. We conducted studies to (1) estimate the frequency of injection use and of poor injection practices, (2) estimate the consequences of poor injection practices in terms of death and disability, (3) formulate best infection control practices for intradermal, subcutaneous and intramuscular injections, (4) quantify the effectiveness of interventions to reduce unnecessary and unsafe use of injections and (5) estimate the cost-effectiveness of national policies for the safe and appropriate use of injections. WHO's Global Burden of Disease project defined 14 regions based on geography and mortality patterns. The analysis excluded four regions (predominantly affluent, developed nations) where reuse of injection equipment in the absence of sterilization was assumed to be negligible. To estimate the frequency of poor injection practices in the year 2000, data sources included published studies and unpublished WHO reports. Studies were reviewed using a standardized decision-making algorithm based upon the quality of the data to generate region-specific estimates of the annual number of injections per person and of the proportion of injections reused in the absence of sterilization. To estimate the consequences of unsafe injections in the year 2000 in terms of death and disability for 2000-2030 as part of the 2000 update of WHO’s Global Burden of Disease study, we modelled the fraction of new injection-associated HBV, HCV and HIV infections on the basis of the annual number of injections, the proportion of injections administered with reused equipment, the probability of transmission following percutaneous exposure, the prevalence of active infection, the prevalence of immunity and the total incidence. Infections in 2000 were converted into disability-adjusted life years (DALYs) in 2000-2030 using natural history parameters, background mortality, duration of disease, disability weights, age weights and a 3% discount rate. A guideline development group summarized evidence-based best practices to prevent injectionassociated infections in resource-limited settings. The development process included (1) a breakdown of the WHO reference injection safety definition into a list of potentially critical steps, (2) a review of the literature for each of these potentially critical steps, (3) the formulation of best practices and (4) the submission of the draft document to peer review. To estimate the effectiveness of interventions to reduce the unnecessary and unsafe use of injections, we searched electronic databases. In addition, we reviewed WHO reports and unpublished assessments made available to WHO. We selected studies that contained quantitative and qualitative information on the effect of interventions and that provided information on study design, type of interventions, targeted participants and targeted behaviours. To estimate the cost-effectiveness of national policies for the safe and appropriate use of injections, the consequences in 2000-2030 of a "do nothing" scenario for the year 2000 (as modelled for the Global Burden of Disease study) were compared to a set of counterfactual scenarios incorporating the health gains of effective interventions. Resources needed to implement effective interventions were costed for each sub-region and expressed in international dollars (I$). Four regions in the Global Burden of Disease study where reuse of injection equipment in the absence of sterilization was negligible were excluded from the analysis. In the 10 other regions, the annual ratio of injections per person was 3.4 (Range: 1.7 - 11.3) for a total of 16.7 thousand million injections received. Of these, 39.3 equipment reused in the absence of sterilization. Reuse was highest in the South East Asia region “D” (seven countries, mostly located in South Asia), the Eastern Mediterranean region “D” (nine countries, mostly located in the Middle East crescent) and the Western Pacific region “B” (22 countries) which together accounted for 88.4 year 2000 with equipment reused in the absence of sterilization. In 2000, contaminated injections caused an estimated 21 million HBV infections, two million HCV infections and 260 000 HIV infections, accounting for 32 9 177 679 DALYs between 2000 and 2030. Eliminating unnecessary injections is the highest priority to prevent injection-associated infections. However, when intradermal, subcutaneous or intramuscular injections are medically indicated, best infection control practices include (1) the use of sterile injection equipment, (2) the prevention of contamination of injection equipment and medication, (3) the prevention of needle-stick injuries to the provider and (4) the prevention of access to used needles. We identified twenty-one articles, abstracts, unpublished reports and assessments containing information on the effectiveness of interventions aiming at reducing injection use (n=19) and at decreasing the unsafe use of injections (n=5). Studies showed a reduction in injection use ranging from 1 of injection equipment in the absence of sterilization reported an absolute decrease of 30 in the intervention groups (relative decrease: 40-100 2000 for the safe (provision of single use syringes, assumed effectiveness: 95 use (patients-providers interactional group discussions, assumed effectiveness: 30 could reduce the burden of injection-associated infections by as much as 96.5 DALYs) for an average yearly cost of I$ million 905 (average cost-effectiveness per DALY averted: I$102, range by region: 14-2 293). In 2000, in developing and transitional countries, 16 thousand million injections were administered for a ratio of 3.4 injections per person. More than a third of all these injections were administered with injection equipment reused in the absence of sterilization, accounting for a substantial burden of infection with bloodborne pathogens. Best infection control practices could make injections safer for the recipient, the health care workers and the community, all the more as effective interventions are available to reduce injection use and to achieve a safe use of injections. These interventions can also be considered very cost-effective on the basis of a cost per DALY averted that is below one year of average per capita income. Remaining areas of uncertainty include (1) the formulation of routine methods to describe injection use and to quantify needs of injection equipment, (2) the description of unsafe practices in greater detail to prevent all opportunities of transmission, (3) the need to generate better estimates of the proportion of HIV infections that may be attributed to unsafe health care injections, (4) the identification of the role of engineered technologies in policies to achieve injection safety, (5) the recovery of experience in the scaling-up of successful interventions and (6) the assessment of the cost-effectiveness of scaled-up national interventions

Modelling the hepatitis C virus disease burden among injecting drug users in Scotland

A forward projection model was used to estimate the numbers of, both current and former, IDUs who acquired HCV infection and progressed to mild, moderate and severe HCV disease in Glasgow and Scotland between 1960 and 2030. The model was developed initially for Glasgow because more epidemiological information exists for this region, than elsewhere in Scotland, to calibrate model outcomes with local data relating to HCV and its consequence. Insights gained from the model fitting process in Glasgow were then used to extend the model to the rest of Scotland. First, the incidence and cessation of injecting drug use in Glasgow during 1960-2000 were derived through the use of a modified Delphi approach. Instead of the usual iterative process to refine experts’ estimates, the elicitation of IDU incidence and cessation provided an opportunity to combine these data and examine coherence with capture-recapture IDU prevalence estimates. Coherent estimates indicated that incidence (median: 28 to 49) and cessation (1 to 24%) remained low and stable during 1960-1975, rose steeply between 1975-1985 (incidence from 49 to 1,335; cessation from 2% to 6%), and by 2000 there had been a decline in incidence (1,195) but a further rise in cessation (15%). Secondly, stochastic simulation was used to model the transmission of HCV among current IDUs in Glasgow, according to their injecting risk behaviours, and estimate the past incidence of HCV infection. The model that considered higher infectivity during acute viraemia following infection produced seroprevalences (median: 62-72%) and incidences (18-30 per 100 susceptible injector-years) consistent with observed data during the 1990s. The annual number of new HCV infections among current IDUs in Glasgow was estimated to be low during 1960-1976 (median: 10-60), rise steeply during 1960-1976 (median: 10-60), rise steeply during the early 1980s to peak in 1985 (1,120), stabilise during 1991-1997 (510-610) and rise again during 1998-2000 (710-780). Scenario analyses indicated that potentially as many as 4,500 HCV infections (10th and 90th percentiles: 2,400-7,700) had been prevented in Glasgow during 1988-2000 as a result of harm-reduction measures. Scenario analyses also permitted the gauging of changes in risk behaviours required to effect appreciable reductions in the incidence of HCV infection. Incidence can be successfully reduced if IDUs who, unavoidably, share needles/syringes confine their borrowing to one person; with this strategy alone, an estimated 5,300 HCV infections (10th and 90th percentiles: 4,100-6,700) could have been averted in Glasgow during 1988-2000. Such insights will inform those responsible for developing new ways to prevent HCV transmission among IDU populations. Thirdly, linkage of laboratory data on diagnosed HCV antibody positive persons in Scotland to clinical data from hospital and death records provided a unique national epidemiological dataset to estimate the number who had progressed to severe HCV disease

Time series modelling of diabetes disease in Taraba State, Nigeria

In this study, we applied an Autoregressive Integrated Moving Average (ARIMA) model to predict the spread of Diabetes disease infection in Taraba State, Nigeria. The monthly recorded cases of Diabetes between January 2010 and December 2020 in Federal Medical Centre, Jalingo was used to fit and validate the ARIMA model. A seasonal fluctuation and a slightly increasing pattern of a long-term trend were revealed in the time series of Diabetes disease. ARIMA (0,1,1) was selected as the best optimal model which has the lowest value of AIC/BIC. The root mean square error (RMSE) was used to assessed the predictive capability of the optimal model. The twenty-four (24) months forecast of Diabetes disease infection in Taraba State, Nigeria was also presented. The ARIMA model could be applied to effectively predict the short-term Diabetes disease infections in Taraba State, Nigeria and provide support for the development of interventions for disease control and prevention