These frequencies were used to simulate patients to find the likeliest paths of symptom onset for discernible symptoms of COVID-19. The dataset from Shanghai, Osaka, Atlanta, and New York contains 249, 244, 531, and 393 patients, respectively. (XLSX)

The columns of the frequencies correspond to dataset. From left to right, they represent a dataset containing patients with COVID-19 and comorbidities in China [33], patients with COVID-19 and comorbidities in the USA [31], patients with COVID-19 and cancer in China [36], patients with COVID-19 and cancer in the USA [37], patients with COVID-19 and COPD in the USA [35], and patients with COVID-19 and HIV in the USA [34]. These frequencies were used to simulate patients to find the likeliest paths of symptom onset for discernible symptoms of COVID-19. The dataset representing patients with comorbidities from China and the USA contains 399 and 463, respectively. The dataset representing patients with cancer from China and the USA contains 205 and 423, respectively. The dataset representing patients with COPD and HIV contains 164 and 93, respectively. (XLSX)

Theories of word learning differentially weigh the role of repeated experience with a novel item, leading to internalization of statistical regularities over time, and the learners use of prior knowledge to infer in-the-moment. Bayesian theories suggest both are critical, but which is weighed more heavily depends on how ambiguous the situation is. To examine this interplay and how it relates to memory, we adapted a Bayesian model of learning (Tenanbaum, Kemp, Griffiths, & Goodman, 2011; Xu & Tenanbaum, 2007) to an inferential word learning task of novel animals, as outline in the following article: “Bayesians learn best: an inferred Bayesian model accounts for individual differences in prior knowledge use during word learning.” Briefly, the model used (i) contextual information provided in the task, quantified by collecting norms for how informative each trial was (likelihood) and (ii) participant’s trial selection accuracy (posterior distribution) to (iii) infer their prior distribution, a proxy for their belief before exposure to the contextual information. Trial accuracy data for the word learning task was collected on one day, and free recall and recognition memory of learned animal names was completed the next day. Norms for how informative each trial was to guide correct selection were collected in a single session with a separate group of participants. Primary data include trial informativeness norms and trial accuracy in the task, both of which were used as input for the Bayesian model. The model infers prior distribution shape parameters from task accuracy and trial norms, completed using the Excel add-in Solver. This is also included in the primary dataset. Output of the model were used to mathematically derive measures of central tendency and spread for participants’ inferred prior distributions, included in the Secondary dataset. These values, along with average block accuracy, were regressed for each participant to examine change across the task. Output from these regressions (slope, intercept and error terms) were used in the statistical analyses with memory measures, which can be found in the Secondary data.

spatial_mathematical_modelThe file contains 1) a system of ordinary differential equations used in the model and 2). a model runner that calls the function

These are the results from 10000 simulations of the CMV stochastic ODE model. Replication code and analysis available on github at: https://github.com/bryanmayer/CMV-Transient-Infections

This fileset contains two datasets Book1_regression.xlsx and Book2.xlsx, in .xlsx file format, and the code file, Corona_research.mw, in .mw file format, used to generate the datasets.The datasets and code support the findings in the following preprint, which is under consideration at BMC Infectious Diseases of Poverty: Atiqur Chowdhury, K M Ariful Kabir, Jun Tanimoto. How quarantine and social distancing policy can suppress the outbreak of novel coronavirus in developing or under poverty level countries: a mathematical and statistical analysis. Research Square https://doi.org/10.21203/rs.3.rs-20294/v1 (2020).

Dataset Book1_regression.xlsx: The first column of this spreadsheet consists of the actual data for the total number of COVID-19 infected people in Bangladesh until March 28th, 2020. Data reported in the first column were collected from www.iedcr.gov.bd from March 7th, 2020 to March 28th, 2020.

The second column of this spreadsheet reports simulated data on the number of COVID-19 infected people, using a novel theoretical model named “Social distancing SEIQR model”. The code used to generate these data is contained in the code file Corona_research.mw.

Dataset Book2.xlsx: This dataset contains data derived using the social distancing SEIQR mathematical model. Data are reported for COVID-19 cases under the following conditions: no lockdown, one week lockdown, two weeks lockdown and three weeks lockdown. Additionally, data are reported for COVID-19 cases under the following conditions: 50%, 40%, 30%, 20% and 10% of people respectively, are following the social distancing rules. The last part of the spreadsheet contains data for COVID-19 cases under the following conditions: 10%, 20%, 30%, 40% 50% and 60% of people respectively, are in home quarantine. The code used to generate these data is contained in the code file Corona_research.mw.

Study aims and methodology: The novel coronavirus, named as SARS-CoV-2, is a transmissible viral disease that has rapidly spread around the world and is currently a major threat for developing and under poverty level countries according to World Bank's and WHO's prediction. At the moment, there is no vaccine or effective treatment to prevent the spread of the virus. However, the policies “self-isolation”, “quarantine”, “lock-down” and “social distancing”, that have been introduced in many countries around the world, aim to control the severity of the outbreak. This paper introduces a novel theoretical model named Social distancing SEIQR model, to control the spread of infection by combining both quarantine and social distancing explicitly based on the real cases observed, in countries where medical equipment and other resources are limited.

The SEIQR social distancing model is based on six assumptions or facts. The basic reproduction number R0, was used to assess the transmissibility of COVID-19. For more details on the methodology and statistical analysis, please read the related article.

Please refer to the published manuscript for methods associated with data collection and analysis.

Appendix A Derivation of contact tracing terms with early and late infectious individuals, Appendix B Modeling social and hygiene measures and changes in the tracing coverage, Appendix C Parameterization, Appendix D Stability analysis. (ZIP)

Background: This study aims to estimate the total number of infected people, evaluate the effects of NPIs on the healthcare system, and predict the expected number of cases, deaths, hospitalizations due to COVID-19 in Turkey.Methods: This study was carried out according to three dimensions. In the first, the actual number of infected people was estimated. In the second, the expected total numbers of infected people, deaths, hospitalizations have been predicted in the case of no intervention. In the third, the distribution of the expected number of infected people and deaths, and ICU and non-ICU bed needs over time has been predicted via a SEIR-based simulator (TURKSAS) in four scenarios.Results: According to the number of deaths, the estimated number of infected people in Turkey on March 21 was 123,030. In the case of no intervention the expected number of infected people is 72,091,595 and deaths is 445,956, the attack rate is 88.1%, and the mortality ratio is 0.54%. The ICU bed capacity in Turkey is expected to be exceeded by 4.4-fold and non-ICU bed capacity by 3.21-fold. In the second and third scenarios compliance with NPIs makes a difference of 94,303 expected deaths. In both scenarios, the predicted peak value of occupied ICU and non-ICU beds remains below Turkey's capacity.Discussion: Predictions show that around 16 million people can be prevented from being infected and 94,000 deaths can be prevented by full compliance with the measures taken. Modeling epidemics and establishing decision support systems is an important requirement.

The Dengue virus (DENV) constitutes a major vector borne virus disease worldwide. Prediction of the DENV spread dynamics, prevalence and infection rates are crucial elements to guide the public health services effort towards meaningful actions. The existence of four DENV serotypes further complicates the virus proliferation forecast. The different serotypes have varying clinical impacts, and the symptomatology of the infection is dependent on the infection history of the patient. Therefore, changes in the prevalent DENV serotype found in one location have a profound impact on the regional public health. The prediction of the spread and intensity of infection of the individual DENV serotypes in specific locations would allow the authorities to plan local pesticide spray to control the vector as well as the purchase of specific antibody therapy. Here we used a mathematical model to predict serotype-specific DENV prevalence and overall case burden in Mexico.

The percentage of binding for each virus type at 1 hour for three data sets each; taken from the original data that were used for Fig 6A in [26].

The paper presents an innovative computational framework for predictive solutions for simulating the spread of malaria. The structure incorporates sophisticated computing methods to improve the reliability of predicting malaria outbreaks. The study strives to provide a strong and effective tool for forecasting the propagation of malaria via the use of an AI-based recurrent neural network (RNN). The model is classified into two groups, consisting of humans and mosquitoes. To develop the model, the traditional Ross-Macdonald model is expanded upon, allowing for a more comprehensive analysis of the intricate dynamics at play. To gain a deeper understanding of the extended Ross model, we employ RNN, treating it as an initial value problem involving a system of first-order ordinary differential equations, each representing one of the seven profiles. This method enables us to obtain valuable insights and elucidate the complexities inherent in the propagation of malaria. Mosquitoes and humans constitute the two cohorts encompassed within the exposition of the mathematical dynamical model. Human dynamics are comprised of individuals who are susceptible, exposed, infectious, and in recovery. The mosquito population, on the other hand, is divided into three categories: susceptible, exposed, and infected. For RNN, we used the input of 0 to 300 days with an interval length of 3 days. The evaluation of the precision and accuracy of the methodology is conducted by superimposing the estimated solution onto the numerical solution. In addition, the outcomes obtained from the RNN are examined, including regression analysis, assessment of error autocorrelation, examination of time series response plots, mean square error, error histogram, and absolute error. A reduced mean square error signifies that the model’s estimates are more accurate. The result is consistent with acquiring an approximate absolute error close to zero, revealing the efficacy of the suggested strategy. This research presents a novel approach to solving the malaria propagation model using recurrent neural networks. Additionally, it examines the behavior of various profiles under varying initial conditions of the malaria propagation model, which consists of a system of ordinary differential equations.