Predicting the death rate around the world due to Covid-19 using regression analysis

Nowadays, COVID-19 is considered to be the biggest disaster that the world is facing. It has created a lot of destruction in the whole world. Due to this COVID-19, analysis has been done to predict the death rate and infected rate from the total population. To perform the analysis on COVID-19, regression analysis has been implemented by applying the differential equation and ordinary differential equation (ODE) on the parameters. The parameters taken for analysis are the number of susceptible individuals, the number of Infected Individuals, and the number of Recovered Individuals. This work will predict the total cases, death cases, and infected cases in the near future based on different reproductive rate values. This work has shown the comparison based on 4 different productive rates i.e. 2.45, 2.55, 2.65, and 2.75. The analysis is done on two different datasets; the first dataset is related to China, and the second dataset is associated with the world's data. The work has predicted that by 2020-08-12: 59,450,123 new cases and 432,499,003 total cases and 10,928,383 deaths.

Disaggregating proportional multistate lifetables by population heterogeneity to estimate intervention impacts on inequalities

Background Simulation models can be used to quantify the projected health impact of interventions. Quantifying heterogeneity in these impacts, for example by socioeconomic status, is important to understand impacts on health inequalities. We aim to disaggregate one type of Markov macro-simulation model, the proportional multistate lifetable, ensuring that under business-as-usual (BAU) the sum of deaths across disaggregated strata in each time step returns the same as the initial non-disaggregated model. We then demonstrate the application by deprivation quintiles for New Zealand (NZ), for: hypothetical interventions (50% lower all-cause mortality, 50% lower coronary heart disease mortality) and a dietary intervention to substitute 59% of sodium with potassium chloride in the food supply. Methods We developed a disaggregation algorithm that iteratively rescales mortality, incidence and case-fatality rates by time-step of the model to ensure correct total population counts were retained at each step. To demonstrate the algorithm on deprivation quintiles in NZ, we used the following inputs: overall (non-disaggregated) all-cause mortality & morbidity rates, coronary heart disease incidence & case fatality rates; stroke incidence & case fatality rates. We also obtained rate ratios by deprivation for these same measures. Given all-cause and cause-specific mortality rates by deprivation quintile, we derived values for the incidence, case fatality and mortality rates for each quintile, ensuring rate ratios across quintiles and the total population mortality and morbidity rates were returned when averaged across groups. The three interventions were then run on top of these scaled BAU scenarios. Results The algorithm exactly disaggregated populations by strata in BAU. The intervention scenario life years and health adjusted life years (HALYs) gained differed slightly when summed over the deprivation quintile compared to the aggregated model, due to the stratified model (appropriately) allowing for differential background mortality rates by strata. Modest differences in health gains (HALYs) resulted from rescaling of sub-population mortality and incidence rates to ensure consistency with the aggregate population. Conclusion Policy makers ideally need to know the effect of population interventions estimated both overall, and by socioeconomic and other strata. We demonstrate a method and provide code to do this routinely within proportional multistate lifetable simulation models and similar Markov models.

Serious role of non-quarantined COVID-19 patients for random walk simulations

The infectious disease (COVID-19) causes serious damages and outbreaks. A large number of infected people have been reported in the world. However, such a number only represents those who have been tested; e.g. PCR test. We focus on the infected individuals who are not checked by inspections. The susceptible-infected-recovered (SIR) model is modified: infected people are divided into quarantined (Q) and non-quarantined (N) agents. Since N-agents behave like uninfected people, they can move around in a stochastic simulation. Both theory of well-mixed population and simulation of random-walk reveal that the total population size of Q-agents decrease in spite of increasing the number of tests. Such a paradox appears, when the ratio of Q exceeds a critical value. Random-walk simulations indicate that the infection hardly spreads, if the movement of all people is prohibited ("lockdown"). In this case the infected people are clustered and locally distributed within narrow spots. The similar result can be obtained, even when only non-infected people move around. However, when both N-agents and uninfected people move around, the infection spreads everywhere. Hence, it may be important to promote the inspections even for asymptomatic people, because most of N-agents are mild or asymptomatic.

The effect of COVID certificates on vaccine uptake, public health, and the economy

In the COVID-19 pandemic, governments have used various interventions,1,2 including COVID certificates as proof of vaccination, recovery, or a recent negative test, required for individuals to access shops, restaurants, and education or workplaces.3 While arguments for and against COVID certificates have focused on reducing transmission and ethical concerns,4,5 the effect of the certificates on vaccine uptake, public health, and the economy requires investigation. We construct counterfactuals based on innovation diffusion theory6 and validate them with econometric methods7 to evaluate the impact of incentives created by COVID certificates in France, Germany, and Italy. We estimate that from their announcement during summer 2021 to the end of the year, the intervention led to increased vaccine uptake in France of 13.0 (95% CI 9.7–14.9) percentage points (p.p.) of the total population, in Germany 6.2 (2.6–6.9) p.p., and in Italy 9.7 (5.4–12.3) p.p.; averted an additional 3,979 (3,453–4,298) deaths in France (i.e., 31.7%), 1,133 (-312–1,358) in Germany (5.6%), and 1,331 (502–1,794) in Italy (14.0%); and prevented gross domestic product (GDP) losses of €6.0 (5.9–6.1) billion in France, €1.4 (1.3–1.5) billion in Germany, and €2.1 (2.0–2.2) billion in Italy. Notably, the application of COVID certificates substantially reduced the pressure on intensive care units (ICUs) and, in France, averted surpassing the occupancy levels where prior lockdowns were instated. Overall, our findings are more substantial than predicted8 and may help to inform decisions about when and how to employ COVID certificates to increase vaccination and thus avoid stringent interventions, such as closures, curfews, and lockdowns, with large social and economic consequences.

Retrodicting the rise, spread, and fall of large-scale states in the Old World

Understanding the rise, spread, and fall of large-scale states in the ancient world has occupied thinkers for millennia. However, no comprehensive mechanistic model of state dynamics based on their insights has emerged, leaving it difficult to evaluate empirically or quantitatively the different explanations offered. Here I present a spatially- and temporally-resolved agent-based model incorporating several hypotheses about the behavior of large-scale (>200 thousand km2) agrarian states and steppe nomadic confederations in Afro-Eurasia between the late Bronze and the end of the Medieval era (1500 BCE to 1500 CE). The model tracks the spread of agrarian states as they expand, conquer the territory of other states or are themselves conquered, and, occasionally, collapse. To accurately retrodict the historical record, several key contingent regional technological advances in state military and agricultural efficiencies are identified. Modifying the location, scale, and timing of these contingent developments allows quantitative investigation of historically-plausible alternative trajectories of state growth, spread, and fragmentation, while demonstrating the operation and limits of the model. Under nominal assumptions, the rapid yet staggered increase of agrarian state sizes across Eurasia after 600 BCE occurs in response to intense military pressure from ‘mirror‘ steppe nomadic confederations. Nevertheless, in spite of various technological advances throughout the period, the modeled creation and spread of new agrarian states is a fundamental consequence of state collapse and internal civil wars triggered by rising ‘demographic-structural’ pressures that occur when state territorial growth is checked yet (warrior elite) population growth continues. Together the model’s underlying mechanisms substantially account for the number of states, their duration, location, spread rate, overall occupied area, and total population size for three thousand years.

Opposing trends in survival and recruitment slow the recovery of a historically overexploited fishery

Quantifying temporal variation in demographic rates is a central goal of population ecology. In this study, we analyzed a multidecadal age-structured time series of Arctic charr (Salvelinus alpinus) abundance in Lake Mývatn, Iceland, to infer the time-varying demographic response of the population to reduced harvest in the wake of the fishery's collapse. Our analysis shows that while survival probability of adults increased following the alleviation of harvesting pressure, per capita recruitment consistently declined over most of the study period, until the final three years when it began to increase. The countervailing demographic trends resulted in only limited directional change in the total population size and population growth rate. Rather, the population dynamics were dominated by large interannual variability and a shift towards an older age distribution. Our results are indicative of a slow recovery of the population after its collapse, despite the rising number of adults following relaxed harvest. This underscores the potential for heterogeneous demographic responses to management efforts due to the complex ecological context in which such efforts take place.

ANALYSIS OF VISIBLE INFRARED IMAGING RADIOMETER SUITE CAPABILITY FOR POPULATION ESTIMATION ON JAVA ISLAND

Population data, despite their significance, are often missing or difficult to access, especially in cities/regencies not belonging to the metropolitan areas or centers of various human activities. This hinders practices that are contingent on their availability. In this study, population estimation was carried out using nighttime light imagery generated by the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument. The variable illuminated area was integrated with the population data using linear regression based on an allometric formula so as to produce a regression value, correlation coefficient (r), and coefficient of determination (r2). The average r2 between the illuminated area and the total population was 0.86, indicating a strong correlation between the two variables. Validation using samples of population estimates from three different years yielded an average error of 73% for each city and 7% for the entire study area. The estimation results for the number of residents per city/regency cannot be used as population data due to the high percent error, but for the population on a larger regional scale, in this case, the island of Java, they have a much smaller percent error and can be used as an initial picture of the total population.

Let's End HepC: Modelling Public Health Epidemiological Policies Applied to Hepatitis C in Spain

Background: The WHO has defined international targets toward the elimination of hepatitis C by 2030. Most countries cannot be on track to achieve this goal unless many challenges are surpassed. The Let's End HepC (LEHC) tool aims to contribute to the control of hepatitis C. The innovation of this tool combines the modelling of public health policies (PHP) focused on hepatitis C with epidemiological modelling of the disease, obtaining a unique result that allows to forecast the impact of policy outcomes. The model was applied to several countries, including Spain.Methods: To address the stated objective, we applied the “Adaptive Conjoint Analysis” for PHP decision-making and Markov Chains in the LEHC modelling tool. The tool also aims to be used as an element of health literacy for patient advocacy through gamification mechanisms and country comparability. The LEHC project has been conducted in several countries, including Spain. The population segments comprised in the project are: People Who Inject Drugs (PWID), prisoners, blood products, remnant population.Results: A total of 24 PHP related to hepatitis C were included in the LEHC project. It was identified that Spain had fully implemented 14 of those policies to control hepatitis C. According to LEHC's model forecast, the WHO's Hepatitis C elimination goal on reducing the number of patients living with Hepatitis C to 10% can be achieved in Spain by 2026 if current policies are maintained. The model estimates that the total population in Spain, by 2026, is expected to comprise 26,367 individuals living with hepatitis C. Moreover, if the 24 PHP considered for this study are fully implemented in Spain, the elimination goal may be achieved in 2024, with 29,615 individuals living with hepatitis C by that year.Conclusion: The findings corroborate the view that Spain has set great efforts in directing PHP toward Hepatitis C Virus (HCV) elimination by 2030. However, there is still room for improvement, namely in further implementing 10 of the 24 PHP considered for the LEHC project. By maintaining the 14 PHP in force, the LEHC model estimates the HCV elimination in the country by 2026, and by 2024 if further measures are employed to control the disease.

Prevalence of Inherited Procoagulant States in Cerebral Venous Thrombosis and its Correlation with Severity and Outcome

Background Cerebral venous thrombosis (CVT) is one of the important causes of stroke in young adults. It is caused by complete or partial thrombotic occlusion of the cerebral venous sinuses or cortical veins. There are many risk factors associated with this condition, out of which common ones are oral contraceptives use, genetic, or acquired thrombophilias, infections, malignancy, pregnancy, and puerperium. We aimed to study the prevalence of inherited procoagulant states in patients with CVT and correlate these states with the severity and outcome. Materials and Methods It was a prospective observational study of 2 years duration in which 75 patients, 18 to 50 years old, with confirmed CVT were included. The baseline data, imaging findings were recorded for all the patients. After 3 months of the onset of CVT, anticoagulants were stopped and a procoagulant test was done for all patients. Severity was assessed by Glasgow Coma Score (GCS) at the onset of illness. Functional assessments were done using the modified Rankin Scale (mRS) at presentation, at 7 days, 6 weeks, and 3 months. Results In the present study, any procoagulant state was seen in 9 out of 75 patients with CVT that accounted for 12% of the total population. There was no significant correlation between the presence of procoagulant states and severity of illness as assessed by GCS at presentation. The presence of any thrombophilia did not affect the final outcome at 7 days, 6 weeks or 3 months (p = 0.532, p = 0.944 and p = 0.965 respectively) as assessed by modified Rankin Scale (mRS). Conclusion Inherited procoagulant states are an important risk factor for CVT. The presence of an inherited procoagulant state does not have any correlation with the disease severity and outcome.

Effect of Vaccination on Household Transmission of SARS-CoV-2 Delta VOC

The SARS-CoV-2 Delta variant of concern (VOC), which has shown increased transmission compared with previous variants, emerged rapidly globally during the first half of 2021, and became one of the most widespread SARS-CoV-2 variants worldwide. We utilized total population data from 24,693 Danish households with 53,584 potential secondary cases to estimate household transmission of the Delta VOC in relation to vaccination status. We found that the vaccine effectiveness against susceptibility (VES) was 61\% (95\%-CI: 59-63) and that the vaccine effectiveness against transmissibility (VET) was 42\% (95\%-CI: 39-45). We also found that unvaccinated individuals with an infection exhibited a higher viral load (one third of a standard deviation) compared to fully vaccinated individuals with a breakthrough infection. Our results imply that vaccinations reduce susceptibility as well as transmissibility. The results are important for policy makers to select strategies for reducing transmission of SARS-CoV-2.