Steps
PANDEMIC PREDICTION IS NECESSARY
wE INTRODUCE unit SCORE TO LEARN FROM FLU AND PREDICT COVID
tHIS IS NOT UNEXPECTED SINCE THERE ARE SIMIALRITIES, BUT ALSO NON TRIVIAL BECAUSE OF DIFFERENCES
cURRENT APPROACHES EITHETR FO THEORY, OR NAIVE ml OR REGRESSION
We begin with sequential incidence data from insurance claims records spanning a decade. Using these incidence data we first compute a simialrity between counties. This measure of simialrity is not arbitrary: our measure is very nearly a metric, and uses diverge of stochastic processes, a generalization of KL diveregnce between distributions. This simialrity map identifies clusters of counties, and identify one such cluster as having the highest initiation risk of seasonal flu. We then reduce the incidence patterns in this counties to a markov model or PFSA. Once we have a simple model of the incidence patern in high risk regions, we compute the loglikelihood of the sequence sbeing generated by this model. This is the raw UNIT risk. The final risk measure is obtained by local scaling with a pertinent epidelimologcal variable, in the case of computing case counts it is % urban population. In case of deaths it is %pop over 65.
Let W be teh set of time series. or approach is:
W --> \cap_i W_i
High risk --> i^\star \in {1,2, .. i, .. n}
W_{i^\star} --> G
\forall x \in W, x||G --> u_0
u --> u_0 X v_urabn
Literature
@article{bertozzi2020challenges,
title={The challenges of modeling and forecasting the spread of COVID-19},
author={Bertozzi, Andrea L and Franco, Elisa and Mohler, George and Short, Martin B and Sledge, Daniel},
journal={arXiv preprint arXiv:2004.04741},
year={2020}
}The world is in the midst of an ongoing pandemic, caused by the emergence of a novel coronavirus. Pharmaceutical interventions such as vaccination and antiviral drugs are not currently available. Over the next year, addressing the coronavirus disease 2019 (COVID-19) outbreak will depend critically on the successful implementation of public health measures including social distancing, shelter in place orders, disease surveillance, contact tracing, isolation, and quarantine (1, 2).
@article{phelan2020novel,
title={The novel coronavirus originating in Wuhan, China: challenges for global health governance},
author={Phelan, Alexandra L and Katz, Rebecca and Gostin, Lawrence O},
journal={Jama},
volume={323},
number={8},
pages={709--710},
year={2020},
publisher={American Medical Association}
}
@misc{pan2020association,
title={Association of Public Health Interventions With the Epidemiology of the COVID-19 Outbreak in Wuhan, China. JAMA},
author={Pan, A and Liu, L and Wang, C and Guo, H and Hao, X and Wang, Q and Huang, J and He, N and Yu, H and Lin, X and others},
year={2020}
}The epidemiological perspective on modeling infectious disease spread involves consideration of a larger number of modeling parameters detailing the spread of and recovery from the disease, additional compartments corresponding to age categories, and other related choices (e.g., refs. 3 and 13). A data-driven approach to modeling COVID-19 has also emerged, in which statistical and machine learning models are used for forecasting cases, hospitalizations, deaths, and impacts of social distancing (14, 15). Our work demonstrates the utility of parsimonious epidemic models for understanding the pandemic and provides an accessible framework for a larger group of quantitative scientists to follow and forecast the COVID-19 pandemic.
%14
@article{altieri2020curating,
title={Curating a COVID-19 data repository and forecasting county-level death counts in the United States},
author={Altieri, Nick and Barter, Rebecca L and Duncan, James and Dwivedi, Raaz and Kumbier, Karl and Li, Xiao and Netzorg, Robert and Park, Briton and Singh, Chandan and Tan, Yan Shuo and others},
journal={arXiv preprint arXiv:2005.07882},
year={2020}
}
%15
@article{covid2020forecasting,
title={Forecasting COVID-19 impact on hospital bed-days, ICU-days, ventilator-days and deaths by US state in the next 4 months},
author={COVID, IHME and Murray, Christopher JL and others},
journal={MedRxiv},
year={2020},
publisher={Cold Spring Harbor Laboratory Press}
}A. Exponential Growth. Epidemics naturally exhibit exponential behavior in the early stages of an outbreak, when the number of infections is already substantial but recoveries and deaths are still negligible.
B. Self-Exciting Branching Process. A branching point process (23⇓–25) can also model the rate of infections over time. Point process models are data driven and allow for parametric or nonparametric estimation of the reproduction number and transmission timescale.
C. Compartmental Models. The SIR model (40⇓–42) describes a classic “compartmental” model with SIR population groups. A related model, susceptible–exposed–infected–resistant (SEIR), includes an “exposed” compartment that models a delay between exposure and infectiousness.
@book{brauer2019mathematical,
title={Mathematical models in epidemiology},
author={Brauer, Fred and Castillo-Chavez, Carlos and Feng, Zhilan},
year={2019},
publisher={Springer}
}
@article{kermack1927contribution,
title={A contribution to the mathematical theory of epidemics},
author={Kermack, William Ogilvy and McKendrick, Anderson G},
journal={Proceedings of the royal society of london. Series A, Containing papers of a mathematical and physical character},
volume={115},
number={772},
pages={700--721},
year={1927},
publisher={The Royal Society London}
}
@article{tolles2020modeling,
title={Modeling Epidemics With Compartmental Models},
author={Tolles, Juliana and Luong, ThaiBinh},
journal={Jama},
year={2020}
}Our analysis, employing parsimonious models, illustrates several key points. 1) The reproduction number R is highly variable both over time and by location, and this variability is compounded by distancing measures. These variations can be calculated using a stochastic model, and lower R is critical to decreasing strains on health care systems and to creating time to develop effective vaccines and antiviral therapies. 2) Mortality data and confirmed case data have statistics that vary by location and by time depending on testing and on accurate accounting of deaths due to the disease. Differences in collection methods and in the accuracy of morbidity and mortality data can lead to different projected outcomes. 3) Nonpharmaceutical public health interventions (NPIs) such as social distancing and shelter in place orders offer an important means of reducing the virus’s reproduction number. Nonetheless, NPIs may not have a substantial impact on the total number of infections unless sustained over time. Policy makers should be cautious about scaling back distancing measures after early signs of effectiveness.
@article{hernandez2020forecasting,
title={Forecasting of COVID19 per regions using ARIMA models and polynomial functions},
author={Hernandez-Matamoros, Andres and Fujita, Hamido and Hayashi, Toshitaka and Perez-Meana, Hector},
journal={Applied Soft Computing},
volume={96},
pages={106610},
year={2020},
publisher={Elsevier}
}
We propose an algorithm to performed and evaluated the ARIMA model for 145 countries, which are distributed into 6 regions. Then, we construct a model for these regions using the ARIMA parameters, the population per 1M people, the number of cases, and polynomial functions. The proposal is able to predict the COVID-19 cases with a RMSE average of 144.81. The main outcome of this paper is showing a relation between COVID-19 behavior and population in a region, these results show us the opportunity to create more models to predict the COVID-19 behavior using variables as humidity, climate, culture, among others.
In December 2019 in Wuhan, China started the pandemic of COVID-19, commonly known as Coronavirus, which has caused havoc around the world. World Health organization reported on June 7 [1], the virus is in 216 Countries, there are 6 750 521 active cases, and it has produced 395 779 deaths. For this reason, scientists around the world have been focused on topics such detect it [2], prevent it [3], cure it [4], and predict it [5], [6], [7], [8], [9], [10], [11], [12], [13]. To predict the coronavirus different schemes has been applied, for example in [11] proposes an approach, which is based Composite Monte Carlo enhanced by deep learning and fuzzy rule induction to predict the COVID-19, [14] detailed models for forecasting the course of the pandemic, these models demonstrate the utility of parsimonious models for early-time data. Using the official data forecasting, [15] studied the spread of COVID-19, they realized forward prediction and backward inference of the epidemic. [16] applied mathematical models and time-series to describe the outbreak among passengers and crew members on Princess Cruises Ship.
@article{benvenuto2020application,
title={Application of the ARIMA model on the COVID-2019 epidemic dataset},
author={Benvenuto, Domenico and Giovanetti, Marta and Vassallo, Lazzaro and Angeletti, Silvia and Ciccozzi, Massimo},
journal={Data in brief},
pages={105340},
year={2020},
publisher={Elsevier}
}
@article{fong2020finding,
title={Finding an accurate early forecasting model from small dataset: A case of 2019-ncov novel coronavirus outbreak},
author={Fong, Simon James and Li, Gloria and Dey, Nilanjan and Crespo, Rub{\'e}n Gonz{\'a}lez and Herrera-Viedma, Enrique},
journal={arXiv preprint arXiv:2003.10776},
year={2020}
}
@article{ding2020brief,
title={Brief Analysis of the ARIMA model on the COVID-19 in Italy},
author={Ding, Guorong and Li, Xinru and Shen, Yang and Fan, Jiao},
journal={medRxiv},
year={2020},
publisher={Cold Spring Harbor Laboratory Press}
}which is based Composite Monte Carlo enhanced by deep learning and fuzzy rule induction to predict the COVID-19
@article{fong2020composite,
title={Composite Monte Carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction},
author={Fong, Simon James and Li, Gloria and Dey, Nilanjan and Crespo, Rub{\'e}n Gonz{\'a}lez and Herrera-Viedma, Enrique},
journal={Applied Soft Computing},
pages={106282},
year={2020},
publisher={Elsevier}
}According to the transmission characteristics of epidemic at different stages, this paper uses Gaussian distribution theory to construct a new model of coronavirus transmission. By simulating the propagation process of the COVID-19, we found that the curves of proposed model well simulate the official data curves of Hubei, Non-Hubei area of China and also South Korea, Italy, and Iran. The study points out the key factors that affect the spread of the virus, such as the basic reproduction number, virus incubation period, and daily infection number.
@article{li2020propagation,
title={Propagation analysis and prediction of the COVID-19},
author={Li, Lixiang and Yang, Zihang and Dang, Zhongkai and Meng, Cui and Huang, Jingze and Meng, Haotian and Wang, Deyu and Chen, Guanhua and Zhang, Jiaxuan and Peng, Haipeng and others},
journal={Infectious Disease Modelling},
volume={5},
pages={282--292},
year={2020},
publisher={Elsevier}
}self-organizing mechanism dynamical characteristics such as the memory effect, through the autocorrelation function, in the studied epidemiological dynamical systems.
@article{contoyiannis2020universal,
title={A Universal Physics-Based Model Describing COVID-19 Dynamics in Europe},
author={Contoyiannis, Yiannis and Stavrinides, Stavros G and P Hanias, Michael and Kampitakis, Myron and Papadopoulos, Pericles and Picos, Rodrigo and M Potirakis, Stelios},
journal={International Journal of Environmental Research and Public Health},
volume={17},
number={18},
pages={6525},
year={2020},
publisher={Multidisciplinary Digital Publishing Institute}
}Risk Factors for COVID
COVID-19 can affect anyone, and the disease can cause symptoms ranging from mild to very severe. For some other illnesses caused by respiratory viruses (such as influenza), some people may be more likely to have severe illness than others because they have characteristics or medical conditions that increase their risk. These are commonly called “risk factors.” Examples include older age or having certain underlying medical conditions.
CDC is conducting disease surveillance and field investigations to better understand why some people are more likely to develop severe COVID-19 illness. This is one of the top priorities in CDC’s strategy to combat COVID-19. What we learn from these efforts will provide vital information to help CDC scientists and other public health officials make decisions to protect our most vulnerable populations.
Risk for Severe Illness Increases with Age
As you get older, your risk for severe illness from COVID-19 increases. For example, people in their 50s are at higher risk for severe illness than people in their 40s. Similarly, people in their 60s or 70s are, in general, at higher risk for severe illness than people in their 50s. The greatest risk for severe illness from COVID-19 is among those aged 85 or older.
However, this does not mean that the odds of contracting COVID, or that of transmitting COVID increases with age. Implying that populations with older people might not have higher number of infected people. But since testing was limited at teh begining, it could be that people with more severe symtoms were more likely to be diagnosed, implying a population with higher number of older people might seem to have higher rates of teh disease.
Risk Factors
- age
- Race/Ethnicity
- Poverty
- Crowding
Similarity of Flu and COVID
Firstly, COVID-19 and influenza viruses have a similar disease presentation. That is, they both cause respiratory disease, which presents as a wide range of illness from asymptomatic or mild through to severe disease and death.
Secondly, both viruses are transmitted by contact, droplets and fomites. As a result, the same public health measures, such as hand hygiene and good respiratory etiquette (coughing into your elbow or into a tissue and immediately disposing of the tissue), are important actions all can take to prevent infection.
https://www.mayoclinic.org/diseases-conditions/coronavirus/in-depth/coronavirus-vs-flu/art-20490339
The viruses that cause COVID-19 and the flu spread in similar ways. They can both spread between people who are in close contact (within 6 feet, or 2 meters). The viruses spread through respiratory droplets or aerosols released through talking, sneezing or coughing. These droplets can land in the mouth or nose of someone nearby or be inhaled. These viruses can also spread if a person touches a surface with one of the viruses on it and then touches his or her mouth, nose or eyes.
How are Flu and Covid Different?
The speed of transmission is an important point of difference between the two viruses. Influenza has a shorter median incubation period (the time from infection to appearance of symptoms) and a shorter serial interval (the time between successive cases) than COVID-19 virus. The serial interval for COVID-19 virus is estimated to be 5-6 days, while for influenza virus, the serial interval is 3 days. This means that influenza can spread faster than COVID-19.
Further, transmission in the first 3-5 days of illness, or potentially pre-symptomatic transmission –transmission of the virus before the appearance of symptoms – is a major driver of transmission for influenza. In contrast, while we are learning that there are people who can shed COVID-19 virus 24-48 hours prior to symptom onset, at present, this does not appear to be a major driver of transmission.
The reproductive number – the number of secondary infections generated from one infected individual – is understood to be between 2 and 2.5 for COVID-19 virus, higher than for influenza. However, estimates for both COVID-19 and influenza viruses are very context and time-specific, making direct comparisons more difficult.