'algorithmic model' in Infectious Diseases

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An Automated Approach for Finding Spatio-Temporal Patterns of Seasonal Influenza in the United States

From publichealth.jmir.org - July 15, 2021 2:15 AM

Agencies such as the Centers for Disease Control and Prevention (CDC) currently release influenza-like illness incidence data, along with descriptive summaries of simple spatio-temporal patterns and trends.

However, public health researchers, government agencies, as well as the general public, are often interested in deeper patterns and insights into how the disease is spreading, with additional context.

Analysis by domain experts is needed for deriving such insights from incidence data.

Objective: Our goal was to develop an automated approach for finding interesting spatio-temporal patterns in the spread of a disease over a large region, such as regions which have specific characteristics (eg, high incidence in a particular week, those which showed a sudden change in incidence) or regions which have significantly different incidence compared to earlier seasons.

Methods: We developed techniques from the area of transactional data mining for characterizing and finding interesting spatio-temporal patterns in disease spread in an automated manner. A key part of our approach involved using the principle of minimum description length for representing a given target set in terms of combinations of attributes (referred to as clauses); we considered both positive and negative clauses, relaxed descriptions which approximately represent the set, and used integer programming to find such descriptions.

Finally, we designed an automated approach, which examines a large space of sets corresponding to different spatio-temporal patterns, and ranks them based on the ratio of their size to their description length (referred to as the compression ratio).

Results: We applied our methods using minimum description length to find spatio-temporal patterns in the spread of seasonal influenza in the United States using state level influenza-like illness activity indicator data from the CDC. We observed that the compression ratios were over 2.5 for 50% of the chosen sets, when approximate descriptions and negative clauses were allowed. Sets with high compression ratios (eg, over 2.5) corresponded to interesting patterns in the spatio-temporal dynamics of influenza-like illness. Our approach also outperformed description by solution in terms of the compression ratio.

Conclusions: Our approach, which is an unsupervised machine learning method, can provide new insights into patterns and trends in the disease spread in an automated manner. Our results show that the description complexity is an effective approach for characterizing sets of interest, which can be easily extended to other diseases and regions beyond influenza in the US. Our approach can also be easily adapted for automated generation of narratives.

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An Agent-Based Model of the Local Spread of SARS-CoV-2: Modeling Study

From medinform.jmir.org - May 3, 2021 12:50 AM

The spread of SARS-CoV-2, originating in Wuhan, China, was classified as a pandemic by the World Health Organization on March 11, 2020.

The governments of affected countries have implemented various measures to limit the spread of the virus. The starting point of this paper is the different government approaches, in terms of promulgating new legislative regulations to limit the virus diffusion and to contain negative effects on the populations.

Objective: This paper aims to study how the spread of SARS-CoV-2 is linked to government policies and to analyze how different policies have produced different results on public health.

Methods: Considering the official data provided by 4 countries (Italy, Germany, Sweden, and Brazil) and from the measures implemented by each government, we built an agent-based model to study the effects that these measures will have over time on different variables such as the total number of COVID-19 cases, intensive care unit (ICU) bed occupancy rates, and recovery and case-fatality rates. The model we implemented provides the possibility of modifying some starting variables, and it was thus possible to study the effects that some policies (eg, keeping the national borders closed or increasing the ICU beds) would have had on the spread of the infection.

Conclusions: In line with what we expected, the obtained results showed that the countries that have taken restrictive measures in terms of limiting the population mobility have managed more successfully than others to contain the spread of COVID-19. Moreover, the model demonstrated that herd immunity cannot be reached even in countries that have relied on a strategy without strict containment measures.

read the study at https://medinform.jmir.org/2021/4/e24192

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Yes, in line with what we expected. Govt's across the world need to capacity build to be ready for a possible future wave. Further, they should be proactive to predict one and act fast when it starts to come in. They come slow and then are everywhere all of a sudden.

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Analysis reveals SARS-CoV-2 infection causes deregulation of lung cell metabolism

From journals.plos.org - April 14, 2021 9:48 AM

A model has been developed by researchers at Indian Institute of Technology ,Kharagpur predicting alteration in metabolic reaction rates of lung cells post SARS-CoV-2 infection.

"We have used the gene expression of normal human bronchial cells infected with SARS-CoV-2 along with the macromolecular make-up of the virus to create this integrated genome-scale metabolic model. The growth rate predicted by the model showed a very high agreement with experimentally and clinically reported effects of SARS-CoV-2," said Dr Amit Ghosh, Assistant Professor, School of Energy Science and Engineering, IIT Kharagpur who coauthored the paper

The research would lead to a better understanding of metabolic reprogramming and aid the development of better therapeutics to deal with viral pandemics,

Summary:

Metabolic flux analysis in disease biology is opening up new avenues for therapeutic interventions. Numerous diseases lead to disturbance in the metabolic homeostasis and it is becoming increasingly important to be able to quantify the difference in interaction under normal and diseased condition.

While genome-scale metabolic models have been used to study those differences, there are limited methods to probe into the differences in flux between these two conditions. Our method of conducting a differential flux analysis can be leveraged to find which reactions are altered between the diseased and normal state.

We applied this to study the altered reactions in the case of SARS-CoV-2 infection. We further corroborated our results with other multi-omics studies and found significant agreement.

read the paper at https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008860

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A Recursive Model of the Spread of COVID-19

From publichealth.jmir.org - May 13, 2021 12:35 PM

The major medical and social challenge of the 21st century is COVID-19, caused by the novel coronavirus SARS-CoV-2.

Critical issues include the rate at which the coronavirus spreads and the effect of quarantine measures and population vaccination on this rate.

Knowledge of the laws of the spread of COVID-19 will enable assessment of the effectiveness and reasonableness of the quarantine measures used, as well as determination of the necessary level of vaccination needed to overcome this crisis.

Objective: This study aims to establish the laws of the spread of COVID-19 and to use them to develop a mathematical model to predict changes in the number of active cases over time, possible human losses, and the rate of recovery of patients, to make informed decisions about the number of necessary beds in hospitals, the introduction and type of quarantine measures, and the required threshold of vaccination of the population.

Methods: This study analyzed the onset of COVID-19 spread in countries such as China, Italy, Spain, the United States, the United Kingdom, Japan, France, and Germany based on publicly available statistical data. The change in the number of COVID-19 cases, deaths, and recovered persons over time was examined, considering the possible introduction of quarantine measures and isolation of infected people in these countries.

Based on the data, the virus transmissibility and the average duration of the disease at different stages were evaluated, and a model based on the principle of recursion was developed. Its key features are the separation of active (nonisolated) infected persons into a distinct category and the prediction of their number based on the average duration of the disease in the inactive phase and the concentration of these persons in the population in the preceding days.

Results: Specific values for SARS-CoV-2 transmissibility and COVID-19 duration were estimated for different countries. In China, the viral transmissibility was 3.12 before quarantine measures were implemented and 0.36 after these measures were lifted. For the other countries, the viral transmissibility was 2.28-2.76 initially, and it then decreased to 0.87-1.29 as a result of quarantine measures. Therefore, it can be expected that the spread of SARS-CoV-2 will be suppressed if 56%-64% of the total population becomes vaccinated or survives COVID-19.

Conclusions: The quarantine measures adopted in most countries are too weak compared to those previously used in China. Therefore, it is not expected that the spread of COVID-19 will stop and the disease will cease to exist naturally or owing to quarantine measures. Active vaccination of the population is needed to prevent the spread of COVID-19.

Furthermore, the required specific percentage of vaccinated individuals depends on the magnitude of viral transmissibility, which can be evaluated using the proposed model and statistical data for the country of interest.

read the entire paper at https://publichealth.jmir.org/2021/4/e21468/

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ML Based Prediction of Growth in Confirmed COVID-19 Infection Cases in 114 Countries Using Metrics of Non-pharmaceutical Interventions and Cultural Dimensions

From www.jmir.org - May 2, 2021 11:01 PM

National governments worldwide have implemented non-pharmaceutical interventions to control the COVID-19 pandemic and mitigate its effects.

Objective: The aim of this study was to investigate the prediction of future daily national confirmed COVID-19 infection growth—the percentage change in total cumulative cases—across 14 days for 114 countries using non-pharmaceutical intervention metrics and cultural dimension metrics, which are indicative of specific national socio-cultural norms.

Methods: We combined the Oxford COVID-19 Government Response Tracker data set, Hofstede cultural dimensions, and daily reported COVID-19 infection case numbers to train and evaluate five non–time series machine learning models in predicting confirmed infection growth.

We used three validation methods—in-distribution, out-of-distribution, and country-based cross-validation—for the evaluation, each of which was applicable to a different use case of the models.

Results: Our results demonstrate high R2 values between the labels and predictions for the in-distribution method (0.959) and moderate R2 values for the out-of-distribution and country-based cross-validation methods (0.513 and 0.574, respectively) using random forest and adaptive boosting (AdaBoost) regression.

Although these models may be used to predict confirmed infection growth, the differing accuracies obtained from the three tasks suggest a strong influence of the use case.

Conclusions: This study adds to the rapidly growing body of work related to predicting COVID-19 infection rates by introducing an approach that incorporates routinely available data on NPIs and cultural dimensions. Importantly, this study emphasizes the utility of NPIs and cultural dimensions for predicting country-level growth of confirmed infections of COVID-19, which to date have been limited in existing forecasting models. Our findings offer a new direction for the broader inclusion of these types of measures, which are also relevant for other infectious diseases, using non–time series machine learning models. Our experiments also provide insight into validation methods for different applications of the models.

read the entire study at https://www.jmir.org/2021/4/e26628

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