Analysis of a mathematical model for malaria using data-driven approach

• URL: http://arxiv.org/abs/2409.00795v1
• Date: Sun, 1 Sep 2024 18:12:34 GMT
• Title: Analysis of a mathematical model for malaria using data-driven approach
• Authors: Adithya Rajnarayanan, Manoj Kumar,
• Abstract summary: Malaria is one of the deadliest diseases in the world, every year millions of people become victims of this disease and many even lose their lives. Medical professionals and the government could take accurate measures to protect the people only when the disease dynamics are understood clearly.
• Score: 5.437165300369346
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Malaria is one of the deadliest diseases in the world, every year millions of people become victims of this disease and many even lose their lives. Medical professionals and the government could take accurate measures to protect the people only when the disease dynamics are understood clearly. In this work, we propose a compartmental model to study the dynamics of malaria. We consider the transmission rate dependent on temperature and altitude. We performed the steady state analysis on the proposed model and checked the stability of the disease-free and endemic steady state. An artificial neural network (ANN) is applied to the formulated model to predict the trajectory of all five compartments following the mathematical analysis. Three different neural network architectures namely Artificial neural network (ANN), convolution neural network (CNN), and Recurrent neural network (RNN) are used to estimate these parameters from the trajectory of the data. To understand the severity of a disease, it is essential to calculate the risk associated with the disease. In this work, the risk is calculated using dynamic mode decomposition(DMD) from the trajectory of the infected people.

Related papers

• MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation [58.93221876843639]
  This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
  arXiv  Detail & Related papers  (2023-10-04T01:36:30Z)
• An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions [73.4962254843935]
  We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history. This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
  arXiv  Detail & Related papers  (2022-04-18T23:42:18Z)
• EINNs: Epidemiologically-Informed Neural Networks [75.34199997857341]
  We introduce a new class of physics-informed neural networks-EINN-crafted for epidemic forecasting. We investigate how to leverage both the theoretical flexibility provided by mechanistic models as well as the data-driven expressability afforded by AI models.
  arXiv  Detail & Related papers  (2022-02-21T18:59:03Z)
• Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data [50.23363975709122]
  We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data. We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem. We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
  arXiv  Detail & Related papers  (2022-02-18T22:41:51Z)
• Disease Informed Neural Networks [3.198144010381572]
  We introduce Disease Informed Neural Networks (DINNs) DINNs are neural networks capable of learning how diseases spread, forecasting their progression, and finding their unique parameters. We use DINNs to identify the dynamics of 11 highly infectious and deadly diseases.
  arXiv  Detail & Related papers  (2021-10-11T17:32:22Z)
• Digital twins based on bidirectional LSTM and GAN for modelling COVID-19 [8.406968279478347]
  coronavirus 2019 has spread throughout the globe infecting over 100 million people and causing the death of over 2.2 million people. There is an urgent need to study the dynamics of epidemiological models to gain a better understanding of how such diseases spread. Recent advances in machine learning techniques have given rise to neural networks with the ability to learn and predict complex dynamics at reduced computational costs.
  arXiv  Detail & Related papers  (2021-02-03T11:54:24Z)
• Semi-supervised Neural Networks solve an inverse problem for modeling Covid-19 spread [61.9008166652035]
  We study the spread of COVID-19 using a semi-supervised neural network. We assume a passive part of the population remains isolated from the virus dynamics.
  arXiv  Detail & Related papers  (2020-10-10T19:33:53Z)
• OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany [0.19791587637442667]
  We present a novel combination of epidemiological modeling with specialized neural networks. We are able to obtain reliable probabilistic estimates for important disease characteristics, such as generation time, fraction of undetected infections, likelihood of transmission before symptom onset, and reporting delays using a very moderate amount of real-world observations.
  arXiv  Detail & Related papers  (2020-10-01T11:01:49Z)
• A Recurrent Neural Network and Differential Equation Based Spatiotemporal Infectious Disease Model with Application to COVID-19 [3.464871689508835]
  We develop an integrated disease model based on epidemic differential equations (SIR) and recurrent neural networks (RNN) We trained and tested our model on CO-19 data in Italy, and show that it out-performs existing temporal models (fully connected NN, SIR, ARIMA) in 1-day, 3-day, and 1-week ahead forecasting.
  arXiv  Detail & Related papers  (2020-07-14T07:04:57Z)
• A self-supervised neural-analytic method to predict the evolution of COVID-19 in Romania [10.760851506126105]
  We use a recently published improved version of SEIR, which is the classic, established model for infectious diseases. We propose a self-supervised approach to train a deep convolutional network to guess the correct set of ModifiedSEIR model parameters. We find an optimistic result in the case fatality rate for Romania which may be around 0.3% and we also demonstrate that our model is able to correctly predict the number of daily fatalities for up to three weeks in the future.
  arXiv  Detail & Related papers  (2020-06-23T12:00:04Z)
• Finding Patient Zero: Learning Contagion Source with Graph Neural Networks [67.3415507211942]
  Locating the source of an epidemic can provide critical insights into the infection's transmission course. Existing methods use graph-theoretic measures and expensive message-passing algorithms. We revisit this problem using graph neural networks (GNNs) to learn P0.
  arXiv  Detail & Related papers  (2020-06-21T21:12:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.