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1.
Lancet Digit Health ; 4(11): e777-e786, 2022 11.
Article in English | MEDLINE | ID: covidwho-2184864

ABSTRACT

BACKGROUND: Traditional viral illness surveillance relies on in-person clinical or laboratory data, paper-based data collection, and outdated technology for data transfer and aggregation. We aimed to assess whether continuous sensor data can provide an early warning signal for COVID-19 activity as individual physiological and behavioural changes might precede symptom onset, care seeking, and diagnostic testing. METHODS: This multivariable, population-based, modelling study recruited adult (aged ≥18 years) participants living in the USA who had a smartwatch or fitness tracker on any device that connected to Apple HealthKit or Google Fit and had joined the DETECT study by downloading the MyDataHelps app. In the model development cohort, we included people who had participated in DETECT between April 1, 2020, and Jan 14, 2022. In the validation cohort, we included individuals who had participated between Jan 15 and Feb 15, 2022. When a participant joins DETECT, they fill out an intake survey of demographic information, including their ZIP code (postal code), and surveys on symptoms, symptom onset, and viral illness test dates and results, if they become unwell. When a participant connects their device, historical sensor data are collected, if available. Sensor data continue to be collected unless a participant withdraws from the study. Using sensor data, we collected each participant's daily resting heart rate and step count during the entire study period and identified anomalous sensor days, in which resting heart rate was higher than, and step count was lower than, a specified threshold calculated for each individual by use of their baseline data. The proportion of users with anomalous data each day was used to create a 7-day moving average. For the main cohort, a negative binomial model predicting 7-day moving averages for COVID-19 case counts, as reported by the Centers for Disease Control and Prevention (CDC), in real time, 6 days in the future, and 12 days in the future in the USA and California was fitted with CDC-reported data from 3 days before alone (H0) or in combination with anomalous sensor data (H1). We compared the predictions with Pearson correlation. We then validated the model in the validation cohort. FINDINGS: Between April 1, 2020, and Jan 14, 2022, 35 842 participants enrolled in DETECT, of whom 4006 in California and 28 527 in the USA were included in our main cohort. The H1 model significantly outperformed the H0 model in predicting the 7-day moving average COVID-19 case counts in California and the USA. For example, Pearson correlation coefficients for predictions 12 days in the future increased by 32·9% in California (from 0·70 [95% CI 0·65-0·73] to 0·93 [0·92-0·94]) and by 12·2% (from 0·82 [0·79-0·84] to 0·92 [0·91-0·93]) in the USA from the H0 model to the H1 model. Our validation model also showed significant correlations for predictions in real time, 6 days in the future, and 12 days in the future. INTERPRETATION: Our study showed that passively collected sensor data from consenting participants can provide real-time disease tracking and forecasting. With a growing population of wearable technology users, these sensor data could be integrated into viral surveillance programmes. FUNDING: The National Center for Advancing Translational Sciences of the US National Institutes of Health, The Rockefeller Foundation, and Amazon Web Services.


Subject(s)
COVID-19 , Adult , Humans , United States/epidemiology , Adolescent , COVID-19/diagnosis , COVID-19/epidemiology , SARS-CoV-2 , Models, Statistical
2.
Prev Med ; 164: 107127, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-2184533

ABSTRACT

It is well known that the statistical analyses in health-science and medical journals are frequently misleading or even wrong. Despite many decades of reform efforts by hundreds of scientists and statisticians, attempts to fix the problem by avoiding obvious error and encouraging good practice have not altered this basic situation. Statistical teaching and reporting remain mired in damaging yet editorially enforced jargon of "significance", "confidence", and imbalanced focus on null (no-effect or "nil") hypotheses, leading to flawed attempts to simplify descriptions of results in ordinary terms. A positive development amidst all this has been the introduction of interval estimates alongside or in place of significance tests and P-values, but intervals have been beset by similar misinterpretations. Attempts to remedy this situation by calling for replacement of traditional statistics with competitors (such as pure-likelihood or Bayesian methods) have had little impact. Thus, rather than ban or replace P-values or confidence intervals, we propose to replace traditional jargon with more accurate and modest ordinary-language labels that describe these statistics as measures of compatibility between data and hypotheses or models, which have long been in use in the statistical modeling literature. Such descriptions emphasize the full range of possibilities compatible with observations. Additionally, a simple transform of the P-value called the surprisal or S-value provides a sense of how much or how little information the data supply against those possibilities. We illustrate these reforms using some examples from a highly charged topic: trials of ivermectin treatment for Covid-19.


Subject(s)
COVID-19 , Humans , Data Interpretation, Statistical , Bayes Theorem , COVID-19/prevention & control , Probability , Models, Statistical , Confidence Intervals
3.
JMIR Public Health Surveill ; 7(6): e27888, 2021 06 09.
Article in English | MEDLINE | ID: covidwho-2197908

ABSTRACT

BACKGROUND: Prior to the COVID-19 pandemic, US hospitals relied on static projections of future trends for long-term planning and were only beginning to consider forecasting methods for short-term planning of staffing and other resources. With the overwhelming burden imposed by COVID-19 on the health care system, an emergent need exists to accurately forecast hospitalization needs within an actionable timeframe. OBJECTIVE: Our goal was to leverage an existing COVID-19 case and death forecasting tool to generate the expected number of concurrent hospitalizations, occupied intensive care unit (ICU) beds, and in-use ventilators 1 day to 4 weeks in the future for New Mexico and each of its five health regions. METHODS: We developed a probabilistic model that took as input the number of new COVID-19 cases for New Mexico from Los Alamos National Laboratory's COVID-19 Forecasts Using Fast Evaluations and Estimation tool, and we used the model to estimate the number of new daily hospital admissions 4 weeks into the future based on current statewide hospitalization rates. The model estimated the number of new admissions that would require an ICU bed or use of a ventilator and then projected the individual lengths of hospital stays based on the resource need. By tracking the lengths of stay through time, we captured the projected simultaneous need for inpatient beds, ICU beds, and ventilators. We used a postprocessing method to adjust the forecasts based on the differences between prior forecasts and the subsequent observed data. Thus, we ensured that our forecasts could reflect a dynamically changing situation on the ground. RESULTS: Forecasts made between September 1 and December 9, 2020, showed variable accuracy across time, health care resource needs, and forecast horizon. Forecasts made in October, when new COVID-19 cases were steadily increasing, had an average accuracy error of 20.0%, while the error in forecasts made in September, a month with low COVID-19 activity, was 39.7%. Across health care use categories, state-level forecasts were more accurate than those at the regional level. Although the accuracy declined as the forecast was projected further into the future, the stated uncertainty of the prediction improved. Forecasts were within 5% of their stated uncertainty at the 50% and 90% prediction intervals at the 3- to 4-week forecast horizon for state-level inpatient and ICU needs. However, uncertainty intervals were too narrow for forecasts of state-level ventilator need and all regional health care resource needs. CONCLUSIONS: Real-time forecasting of the burden imposed by a spreading infectious disease is a crucial component of decision support during a public health emergency. Our proposed methodology demonstrated utility in providing near-term forecasts, particularly at the state level. This tool can aid other stakeholders as they face COVID-19 population impacts now and in the future.


Subject(s)
COVID-19/therapy , Delivery of Health Care , Health Planning/methods , Hospitalization , Intensive Care Units , Pandemics , Respiration, Artificial , COVID-19/mortality , Equipment and Supplies , Forecasting , Hospitals , Humans , Length of Stay , Models, Statistical , New Mexico , Public Health , SARS-CoV-2 , Surge Capacity
4.
JMIR Public Health Surveill ; 7(6): e26784, 2021 06 01.
Article in English | MEDLINE | ID: covidwho-2197902

ABSTRACT

BACKGROUND: Despite recent achievements in vaccines, antiviral drugs, and medical infrastructure, the emergence of COVID-19 has posed a serious threat to humans worldwide. Most countries are well connected on a global scale, making it nearly impossible to implement perfect and prompt mitigation strategies for infectious disease outbreaks. In particular, due to the explosive growth of international travel, the complex network of human mobility enabled the rapid spread of COVID-19 globally. OBJECTIVE: South Korea was one of the earliest countries to be affected by COVID-19. In the absence of vaccines and treatments, South Korea has implemented and maintained stringent interventions, such as large-scale epidemiological investigations, rapid diagnosis, social distancing, and prompt clinical classification of severely ill patients with appropriate medical measures. In particular, South Korea has implemented effective airport screenings and quarantine measures. In this study, we aimed to assess the country-specific importation risk of COVID-19 and investigate its impact on the local transmission of COVID-19. METHODS: The country-specific importation risk of COVID-19 in South Korea was assessed. We investigated the relationships between country-specific imported cases, passenger numbers, and the severity of country-specific COVID-19 prevalence from January to October 2020. We assessed the country-specific risk by incorporating country-specific information. A renewal mathematical model was employed, considering both imported and local cases of COVID-19 in South Korea. Furthermore, we estimated the basic and effective reproduction numbers. RESULTS: The risk of importation from China was highest between January and February 2020, while that from North America (the United States and Canada) was high from April to October 2020. The R0 was estimated at 1.87 (95% CI 1.47-2.34), using the rate of α=0.07 for secondary transmission caused by imported cases. The Rt was estimated in South Korea and in both Seoul and Gyeonggi. CONCLUSIONS: A statistical model accounting for imported and locally transmitted cases was employed to estimate R0 and Rt. Our results indicated that the prompt implementation of airport screening measures (contact tracing with case isolation and quarantine) successfully reduced local transmission caused by imported cases despite passengers arriving from high-risk countries throughout the year. Moreover, various mitigation interventions, including social distancing and travel restrictions within South Korea, have been effectively implemented to reduce the spread of local cases in South Korea.


Subject(s)
COVID-19/epidemiology , COVID-19/transmission , Communicable Diseases, Imported/epidemiology , Humans , Models, Statistical , Republic of Korea/epidemiology , Risk Assessment
5.
JMIR Public Health Surveill ; 7(3): e21606, 2021 03 18.
Article in English | MEDLINE | ID: covidwho-2197869

ABSTRACT

BACKGROUND: Previous studies on the impact of social distancing on COVID-19 mortality in the United States have predominantly examined this relationship at the national level and have not separated COVID-19 deaths in nursing homes from total COVID-19 deaths. This approach may obscure differences in social distancing behaviors by county in addition to the actual effectiveness of social distancing in preventing COVID-19 deaths. OBJECTIVE: This study aimed to determine the influence of county-level social distancing behavior on COVID-19 mortality (deaths per 100,000 people) across US counties over the period of the implementation of stay-at-home orders in most US states (March-May 2020). METHODS: Using social distancing data from tracked mobile phones in all US counties, we estimated the relationship between social distancing (average proportion of mobile phone usage outside of home between March and May 2020) and COVID-19 mortality (when the state in which the county is located reported its first confirmed case of COVID-19 and up to May 31, 2020) with a mixed-effects negative binomial model while distinguishing COVID-19 deaths in nursing homes from total COVID-19 deaths and accounting for social distancing- and COVID-19-related factors (including the period between the report of the first confirmed case of COVID-19 and May 31, 2020; population density; social vulnerability; and hospital resource availability). Results from the mixed-effects negative binomial model were then used to generate marginal effects at the mean, which helped separate the influence of social distancing on COVID-19 deaths from other covariates while calculating COVID-19 deaths per 100,000 people. RESULTS: We observed that a 1% increase in average mobile phone usage outside of home between March and May 2020 led to a significant increase in COVID-19 mortality by a factor of 1.18 (P<.001), while every 1% increase in the average proportion of mobile phone usage outside of home in February 2020 was found to significantly decrease COVID-19 mortality by a factor of 0.90 (P<.001). CONCLUSIONS: As stay-at-home orders have been lifted in many US states, continued adherence to other social distancing measures, such as avoiding large gatherings and maintaining physical distance in public, are key to preventing additional COVID-19 deaths in counties across the country.


Subject(s)
COVID-19/mortality , COVID-19/prevention & control , Physical Distancing , Cross-Sectional Studies , Humans , Models, Statistical , United States/epidemiology
6.
Elife ; 92020 08 24.
Article in English | MEDLINE | ID: covidwho-2155737

ABSTRACT

A key unknown for SARS-CoV-2 is how asymptomatic infections contribute to transmission. We used a transmission model with asymptomatic and presymptomatic states, calibrated to data on disease onset and test frequency from the Diamond Princess cruise ship outbreak, to quantify the contribution of asymptomatic infections to transmission. The model estimated that 74% (70-78%, 95% posterior interval) of infections proceeded asymptomatically. Despite intense testing, 53% (51-56%) of infections remained undetected, most of them asymptomatic. Asymptomatic individuals were the source for 69% (20-85%) of all infections. The data did not allow identification of the infectiousness of asymptomatic infections, however low ranges (0-25%) required a net reproduction number for individuals progressing through presymptomatic and symptomatic stages of at least 15. Asymptomatic SARS-CoV-2 infections may contribute substantially to transmission. Control measures, and models projecting their potential impact, need to look beyond the symptomatic cases if they are to understand and address ongoing transmission.


Subject(s)
Asymptomatic Diseases , Coronavirus Infections/transmission , Pneumonia, Viral/therapy , Ships/statistics & numerical data , Betacoronavirus/isolation & purification , COVID-19 , Calibration , Coronavirus Infections/epidemiology , Coronavirus Infections/therapy , Humans , Incidence , Models, Statistical , Pandemics , Pneumonia, Viral/epidemiology , SARS-CoV-2
7.
J Healthc Eng ; 2022: 4864920, 2022.
Article in English | MEDLINE | ID: covidwho-2138235

ABSTRACT

COVID-19 continues to pose a dangerous global health threat, as cases grow rapidly and deaths increase day by day. This increasing phenomenon does not only affect economic policy but also international policy around the world. In this paper, Pakistan daily death cases of COVID-19, from February 25, 2020, to March 23, 2022, have been modeled using the long-established autoregressive-integrated moving average (ARIMA) model and the machine learning multilayer perceptron (MLP) model. The most befitting model is selected based on the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). Values of the key performance indicator (KPI) showed that the MLP model outperformed the ARIMA model. The MLP model with 20 hidden layers, which emerged as the overall most apt model, was used to predict future daily COVID-19 deaths in Pakistan to enable policymakers and health professionals to put in place systematic measures to reduce death cases. We encourage the Government of Pakistan to intensify its vaccination campaign and encourage everyone to get vaccinated.


Subject(s)
COVID-19 , Humans , Incidence , Models, Statistical , Neural Networks, Computer , Forecasting
8.
BMC Infect Dis ; 22(1): 833, 2022 Nov 10.
Article in English | MEDLINE | ID: covidwho-2117326

ABSTRACT

Forecasts of the trajectory of an infectious agent can help guide public health decision making. A traditional approach to forecasting fits a computational model to structured data and generates a predictive distribution. However, human judgment has access to the same data as computational models plus experience, intuition, and subjective data. We propose a chimeric ensemble-a combination of computational and human judgment forecasts-as a novel approach to predicting the trajectory of an infectious agent. Each month from January, 2021 to June, 2021 we asked two generalist crowds, using the same criteria as the COVID-19 Forecast Hub, to submit a predictive distribution over incident cases and deaths at the US national level either two or three weeks into the future and combined these human judgment forecasts with forecasts from computational models submitted to the COVID-19 Forecasthub into a chimeric ensemble. We find a chimeric ensemble compared to an ensemble including only computational models improves predictions of incident cases and shows similar performance for predictions of incident deaths. A chimeric ensemble is a flexible, supportive public health tool and shows promising results for predictions of the spread of an infectious agent.


Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , Judgment , Forecasting , Public Health , Computer Simulation , Models, Statistical
9.
PLoS One ; 17(10): e0276688, 2022.
Article in English | MEDLINE | ID: covidwho-2098752

ABSTRACT

The objective of this study is to construct a new distribution known as the weighted Burr-Hatke distribution (WBHD). The PDF and CDF of the WBHD are derived in a closed form. Moments, incomplete moments, and the quantile function of the proposed distribution are derived mathematically. Eleven estimate techniques for estimating the distribution parameters are discussed, and numerical simulations are utilised to evaluate the various approaches using partial and overall rankings. According to the findings of this study, it is recommended that the maximum product of spacing (MPSE) estimator of the WBHD is the best estimator according to overall rank table. The actuarial measurements were derived to the suggested distribution. By contrasting the WBHD with other competitive distributions using two different actual data sets collected from the COVID-19 mortality rates, we show the importance and flexibility of the WBHD.


Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , Saudi Arabia/epidemiology , Models, Statistical
10.
BMC Med Res Methodol ; 22(1): 281, 2022 11 01.
Article in English | MEDLINE | ID: covidwho-2098313

ABSTRACT

BACKGROUND: The aim of this study was to evaluate the most effective combination of autoregressive integrated moving average (ARIMA), a time series model, and association rule mining (ARM) techniques to identify meaningful prognostic factors and predict the number of cases for efficient COVID-19 crisis management. METHODS: The 3685 COVID-19 patients admitted at Thailand's first university field hospital following the four waves of infections from March 2020 to August 2021 were analyzed using the autoregressive integrated moving average (ARIMA), its derivative to exogenous variables (ARIMAX), and association rule mining (ARM). RESULTS: The ARIMA (2, 2, 2) model with an optimized parameter set predicted the number of the COVID-19 cases admitted at the hospital with acceptable error scores (R2 = 0.5695, RMSE = 29.7605, MAE = 27.5102). Key features from ARM (symptoms, age, and underlying diseases) were selected to build an ARIMAX (1, 1, 1) model, which yielded better performance in predicting the number of admitted cases (R2 = 0.5695, RMSE = 27.7508, MAE = 23.4642). The association analysis revealed that hospital stays of more than 14 days were related to the healthcare worker patients and the patients presented with underlying diseases. The worsening cases that required referral to the hospital ward were associated with the patients admitted with symptoms, pregnancy, metabolic syndrome, and age greater than 65 years old. CONCLUSIONS: This study demonstrated that the ARIMAX model has the potential to predict the number of COVID-19 cases by incorporating the most associated prognostic factors identified by ARM technique to the ARIMA model, which could be used for preparation and optimal management of hospital resources during pandemics.


Subject(s)
COVID-19 , Humans , Aged , COVID-19/epidemiology , Time Factors , Models, Statistical , Pandemics , Forecasting , Data Mining
11.
J Evol Biol ; 34(6): 910-923, 2021 06.
Article in English | MEDLINE | ID: covidwho-2097796

ABSTRACT

Climate change is impacting locally adapted species such as the keystone tree species cork oak (Quercus suber L.). Quantifying the importance of environmental variables in explaining the species distribution can help build resilient populations in restoration projects and design forest management strategies. Using landscape genomics, we investigated the population structure and ecological adaptation of this tree species across the Mediterranean Basin. We applied genotyping by sequencing and derived 2,583 single nucleotide polymorphism markers genotyped from 81 individuals across 17 sites in the studied region. We implemented an approach based on the nearest neighbour haplotype 'coancestry' and uncovered a weak population structure along an east-west climatic gradient across the Mediterranean region. We identified genomic regions potentially involved in local adaptation and predicted differences in the genetic composition across the landscape under current and future climates. Variants associated with temperature and precipitation variables were detected, and we applied a nonlinear multivariate association method, gradient forest, to project these gene-environment relationships across space. The model allowed the identification of geographic areas within the western Mediterranean region most sensitive to climate change: south-western Iberia and northern Morocco. Our findings provide a preliminary assessment towards a potential management strategy for the conservation of cork oak in the Mediterranean Basin.


Subject(s)
Adaptation, Biological , Climate Change , Quercus , Ecosystem , Gene-Environment Interaction , Mediterranean Region , Models, Statistical , Polymorphism, Single Nucleotide
12.
Sci Rep ; 12(1): 18138, 2022 Oct 28.
Article in English | MEDLINE | ID: covidwho-2096807

ABSTRACT

Globally, since the outbreak of the Omicron variant in November 2021, the number of confirmed cases of COVID-19 has continued to increase, posing a tremendous challenge to the prevention and control of this infectious disease in many countries. The global daily confirmed cases of COVID-19 between November 1, 2021, and February 17, 2022, were used as a database for modeling, and the ARIMA, MLR, and Prophet models were developed and compared. The prediction performance was evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The study showed that ARIMA (7, 1, 0) was the optimum model, and the MAE, MAPE, and RMSE values were lower than those of the MLR and Prophet models in terms of fitting performance and forecasting performance. The ARIMA model had superior prediction performance compared to the MLR and Prophet models. In real-world research, an appropriate prediction model should be selected based on the characteristics of the data and the sample size, which is essential for obtaining more accurate predictions of infectious disease incidence.


Subject(s)
COVID-19 , Pandemics , Humans , COVID-19/epidemiology , SARS-CoV-2 , Incidence , Forecasting , Models, Statistical
13.
Infect Control Hosp Epidemiol ; 42(6): 653-658, 2021 Jun.
Article in English | MEDLINE | ID: covidwho-2096425

ABSTRACT

BACKGROUND: The pressures exerted by the coronavirus disease 2019 (COVID-19) pandemic pose an unprecedented demand on healthcare services. Hospitals become rapidly overwhelmed when patients requiring life-saving support outpace available capacities. OBJECTIVE: We describe methods used by a university hospital to forecast case loads and time to peak incidence. METHODS: We developed a set of models to forecast incidence among the hospital catchment population and to describe the COVID-19 patient hospital-care pathway. The first forecast utilized data from antecedent allopatric epidemics and parameterized the care-pathway model according to expert opinion (ie, the static model). Once sufficient local data were available, trends for the time-dependent effective reproduction number were fitted, and the care pathway was reparameterized using hazards for real patient admission, referrals, and discharge (ie, the dynamic model). RESULTS: The static model, deployed before the epidemic, exaggerated the bed occupancy for general wards (116 forecasted vs 66 observed), ICUs (47 forecasted vs 34 observed), and predicted the peak too late: general ward forecast April 9 and observed April 8 and ICU forecast April 19 and observed April 8. After April 5, the dynamic model could be run daily, and its precision improved with increasing availability of empirical local data. CONCLUSIONS: The models provided data-based guidance for the preparation and allocation of critical resources of a university hospital well in advance of the epidemic surge, despite overestimating the service demand. Overestimates should resolve when the population contact pattern before and during restrictions can be taken into account, but for now they may provide an acceptable safety margin for preparing during times of uncertainty.


Subject(s)
COVID-19/epidemiology , Hospital Bed Capacity , Hospitals, University/organization & administration , COVID-19/prevention & control , Cross Infection/prevention & control , Forecasting , Germany/epidemiology , Hospitals, University/statistics & numerical data , Humans , Incidence , Models, Statistical , Patient Safety
14.
PLoS One ; 17(10): e0276181, 2022.
Article in English | MEDLINE | ID: covidwho-2089423

ABSTRACT

Since the spread of COVID-19 pandemic in early 2020, modeling the related factors became mandatory, requiring new families of statistical distributions to be formulated. In the present paper we are interested in modeling the vaccination rate in some African countries. The recorded data in these countries show less vaccination rate, which will affect the spread of new active cases and will increase the mortality rate. A new extension of the inverted Nadarajah-Haghighi distribution is considered, which has four parameters and is obtained by combining the inverted Nadarajah-Haghighi distribution and the odd Lomax-G family. The proposed distribution is called the odd Lomax inverted Nadarajah-Haghighi (OLINH) distribution. This distribution owns many virtuous characteristics and attractive statistical properties, such as, the simple linear representation of density function, the flexibility of the hazard rate curve and the odd ratio of failure, in addition to other properties related to quantile, the rth-moment, moment generating function, Rényi entropy, and the function of ordered statistics. In this paper we address the problem of parameter estimation from frequentest and Bayesian approach, accordingly a comparison between the performance of the two estimation methods is implemented using simulation analysis and some numerical techniques. Finally different goodness of fit measures are used for modeling the COVID-19 vaccination rate, which proves the suitability of the OLINH distribution over other competitive distributions.


Subject(s)
COVID-19 , Models, Statistical , Humans , COVID-19 Vaccines , Bayes Theorem , Pandemics , COVID-19/epidemiology , COVID-19/prevention & control , Vaccination
15.
Isr J Health Policy Res ; 11(1): 36, 2022 10 20.
Article in English | MEDLINE | ID: covidwho-2079545

ABSTRACT

Mathematical and statistical models have played an important role in the analysis of data from COVID-19. They are important for tracking the progress of the pandemic, for understanding its spread in the population, and perhaps most significantly for forecasting the future course of the pandemic and evaluating potential policy options. This article describes the types of models that were used by research teams in Israel, presents their assumptions and basic elements, and illustrates how they were used, and how they influenced decisions. The article grew out of a "modelists' dialog" organized by the Israel National Institute for Health Policy Research with participation from some of the leaders in the local modeling effort.


Subject(s)
COVID-19 , Humans , Pandemics/prevention & control , SARS-CoV-2 , Israel/epidemiology , Models, Statistical
16.
BMJ Open ; 12(10): e056801, 2022 10 17.
Article in English | MEDLINE | ID: covidwho-2078939

ABSTRACT

OBJECTIVE: The Brazilian state of Paraná has suffered from COVID-19 effects, understanding predictors of increased mortality in health system interventions prevent hospitalisation of patients. We selected the best models to evaluate the association of death with demographic characteristics, symptoms and comorbidities based on three levels of clinical severity for COVID-19: non-hospitalised, hospitalised non-ICU ward and ICU ward. DESIGN: Cross-sectional survey using binomial mixed models. SETTING: COVID-19-positive cases diagnosed by reverse transcription-PCR of municipalities located in Paraná State. PATIENTS: Cases of anonymous datasets of electronic medical records from 1 April 2020 to 31 December 2020. PRIMARY AND SECONDARY OUTCOME MEASURES: The best prediction factors were chosen based on criteria after a stepwise analysis using multicollinearity measure, lower Akaike information criterion and goodness-of-fit χ2 tests from univariate to multivariate contexts. RESULTS: Male sex was associated with increased mortality among non-hospitalised patients (OR 1.76, 95% CI 1.47 to 2.11) and non-ICU patients (OR 1.22, 95% CI 1.05 to 1.43) for symptoms and for comorbidities (OR 1.89, 95% CI 1.59 to 2.25, and OR 1.30, 95% CI 1.11 to 1.52, respectively). Higher mortality occurred in patients older than 35 years in non-hospitalised (for symptoms: OR 4.05, 95% CI 1.55 to 10.54; and for comorbidities: OR 3.00, 95% CI 1.24 to 7.27) and in hospitalised over 40 years (for symptoms: OR 2.72, 95% CI 1.08 to 6.87; and for comorbidities: OR 2.66, 95% CI 1.22 to 5.79). Dyspnoea was associated with increased mortality in non-hospitalised (OR 4.14, 95% CI 3.45 to 4.96), non-ICU (OR 2.41, 95% CI 2.04 to 2.84) and ICU (OR 1.38, 95% CI 1.10 to 1.72) patients. Neurological disorders (OR 2.16, 95% CI 1.35 to 3.46), neoplastic (OR 3.22, 95% CI 1.75 to 5.93) and kidney diseases (OR 2.13, 95% CI 1.36 to 3.35) showed the majority of increased mortality for ICU as well in the three levels of severity jointly with heart disease, diabetes and CPOD. CONCLUSIONS: These findings highlight the importance of the predictor's assessment for the implementation of public healthcare policy in response to the COVID-19 pandemic, mainly to understand how non-pharmaceutical measures could mitigate the virus impact over the population.


Subject(s)
COVID-19 , Humans , Male , Brazil/epidemiology , Comorbidity , COVID-19/complications , COVID-19/epidemiology , COVID-19/mortality , COVID-19/therapy , Cross-Sectional Studies , Hospitalization , Intensive Care Units , Pandemics , Female , Risk Factors , Adult , Middle Aged , Aged , Models, Statistical
17.
PLoS Comput Biol ; 18(10): e1010602, 2022 10.
Article in English | MEDLINE | ID: covidwho-2054252

ABSTRACT

We analyze an ensemble of n-sub-epidemic modeling for forecasting the trajectory of epidemics and pandemics. These ensemble modeling approaches, and models that integrate sub-epidemics to capture complex temporal dynamics, have demonstrated powerful forecasting capability. This modeling framework can characterize complex epidemic patterns, including plateaus, epidemic resurgences, and epidemic waves characterized by multiple peaks of different sizes. We systematically assess their calibration and short-term forecasting performance in short-term forecasts for the COVID-19 pandemic in the USA from late April 2020 to late February 2022. We compare their performance with two commonly used statistical ARIMA models. The best fit sub-epidemic model and three ensemble models constructed using the top-ranking sub-epidemic models consistently outperformed the ARIMA models in terms of the weighted interval score (WIS) and the coverage of the 95% prediction interval across the 10-, 20-, and 30-day short-term forecasts. In our 30-day forecasts, the average WIS ranged from 377.6 to 421.3 for the sub-epidemic models, whereas it ranged from 439.29 to 767.05 for the ARIMA models. Across 98 short-term forecasts, the ensemble model incorporating the top four ranking sub-epidemic models (Ensemble(4)) outperformed the (log) ARIMA model 66.3% of the time, and the ARIMA model, 69.4% of the time in 30-day ahead forecasts in terms of the WIS. Ensemble(4) consistently yielded the best performance in terms of the metrics that account for the uncertainty of the predictions. This framework can be readily applied to investigate the spread of epidemics and pandemics beyond COVID-19, as well as other dynamic growth processes found in nature and society that would benefit from short-term predictions.


Subject(s)
COVID-19 , Humans , United States/epidemiology , COVID-19/epidemiology , Pandemics , Forecasting , Models, Statistical , Time
18.
JAMA ; 328(8): 772-773, 2022 08 23.
Article in English | MEDLINE | ID: covidwho-2041181
19.
PLoS One ; 17(9): e0270182, 2022.
Article in English | MEDLINE | ID: covidwho-2039340

ABSTRACT

Governments have implemented different interventions and response models to combat the spread of COVID-19. The necessary intensity and frequency of control measures require us to project the number of infected cases. Three short-term forecasting models were proposed to predict the total number of infected cases in Canada for a number of days ahead. The proposed models were evaluated on how their performance degrades with increased forecast horizon, and improves with increased historical data by which to estimate them. For the data analyzed, our results show that 7 to 10 weeks of historical data points are enough to produce good fits for a two-weeks predictive model of infected case numbers with a NRMSE of 1% to 2%. The preferred model is an important quick-deployment tool to support data-informed short-term pandemic related decision-making at all levels of governance.


Subject(s)
COVID-19 , COVID-19/epidemiology , Canada/epidemiology , Forecasting , Humans , Models, Statistical , Pandemics/prevention & control
20.
Stat Med ; 41(27): 5379-5394, 2022 Nov 30.
Article in English | MEDLINE | ID: covidwho-2027404

ABSTRACT

Personalized medicine aims to tailor medical decisions based on patient-specific characteristics. Advances in data capturing techniques such as electronic health records dramatically increase the availability of comprehensive patient profiles, promoting the rapid development of optimal treatment rule (OTR) estimation methods. An archetypal OTR estimation approach is the outcome weighted learning, where OTR is determined under a weighted classification framework with clinical outcomes as the weights. Although outcome weighted learning has been extensively studied and extended, existing methods are susceptible to irregularities of outcome distributions such as outliers and heavy tails. Methods that involve modeling of the outcome are also sensitive to model misspecification. We propose a contrast weighted learning (CWL) framework that exploits the flexibility and robustness of contrast functions to enable robust OTR estimation for a wide range of clinical outcomes. The novel value function in CWL only depends on the pairwise contrast of clinical outcomes between patients irrespective of their distributional features and supports. The Fisher consistency and convergence rate of the estimated decision rule via CWL are established. We illustrate the superiority of the proposed method under finite samples using comprehensive simulation studies with ill-distributed continuous outcomes and ordinal outcomes. We apply the CWL method to two datasets from clinical trials on idiopathic pulmonary fibrosis and COVID-19 to demonstrate its real-world application.


Subject(s)
COVID-19 , Models, Statistical , Humans , Algorithms , Precision Medicine/methods , Computer Simulation
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