A Novel Approach to Modeling and Forecasting Cancer Incidence and Mortality Rates through Web Queries and Automated Forecasting Algorithms: Evidence from Romania

Abstract

Cancer remains a leading cause of worldwide mortality and is a growing, multifaceted global burden. As a result, cancer prevention and cancer mortality reduction are counted among the most pressing public health issues of the twenty-first century. In turn, accurate projections of cancer incidence and mortality rates are paramount for robust policymaking, aimed at creating efficient and inclusive public health systems and also for establishing a baseline to assess the impact of newly introduced public health measures. Within the European Union (EU), Romania consistently reports higher mortality from all types of cancer than the EU average, caused by an inefficient and underfinanced public health system and lower economic development that in turn have created the phenomenon of “oncotourism”. This paper aims to develop novel cancer incidence/cancer mortality models based on historical links between incidence and mortality occurrence as reflected in official statistics and population web-search habits. Subsequently, it employs estimates of the web query index to produce forecasts of cancer incidence and mortality rates in Romania. Various statistical and machine-learning models—the autoregressive integrated moving average model (ARIMA), the Exponential Smoothing State Space Model with Box-Cox Transformation, ARMA Errors, Trend, and Seasonal Components (TBATS), and a feed-forward neural network nonlinear autoregression model, or NNAR—are estimated through automated algorithms to assess in-sample fit and out-of-sample forecasting accuracy for web-query volume data. Forecasts are produced with the overperforming model in the out-of-sample context (i.e., NNAR) and fed into the novel incidence/ mortality models. Results indicate a continuation of the increasing trends in cancer incidence and mortality in Romania by 2026, with projected levels for the age-standardized total cancer incidence of 313.8 and the age-standardized mortality rate of 233.8 representing an increase of 2%, and, respectively, 3% relative to the 2019 levels. Research findings thus indicate that, under the no-change hypothesis, cancer will remain a significant burden in Romania and highlight the need and urgency to improve the status quo in the Romanian public health system.

Time series analysis and prediction of scarlet fever incidence trends in Jiangsu Province, China: using ARIMA and TBATS models

“…Therefore, it is crucial to strengthen scarlet fever control and preventive measures in schools during April and June. [31,32] .The TBATS model also has other advantages: one is the stability of the model results, and the other is that fewer initial parameters need to be estimated, so the decomposition of the components of the time series is more stable. This gives TBATS the potential to describe the long-term prevalence of scarlet fever, given the epidemiological characteristics of scarlet fever incidence [33] .…”

Comparing the current short-term cancer incidence prediction models in Brazil with state-of-the-art time-series models

“…Tudor 17 proposed alternative ways to forecast cancer incidence and mortality by connecting population web-search practices with health variables officially published by Romanian authorities. The applied models included ARIMA, the Exponential Smoothing State-Space Model with Box-Cox Transformation, ARMA Errors, Trend, and Seasonal Components, and a feed-forward neural network nonlinear autoregression model.…”

Unraveling the Multifaceted Nature of Breast Cancer: Harnessing the Power of Machine Learning and Statistical Models for In-depth Investigation, Prognosis, and Treatment Optimization

“…These are univariate models that are employed in order to gain a deeper comprehension of a single time-dependent variable. [19]the practice of estimating future data points of variables, such as temperature over time. The functionality of these models is predicated on the assumption that the data is stationary.…”