Joint COVID-19 and influenza-like illness forecasts in the United States using internet search information.

BACKGROUND: As the prolonged COVID-19 pandemic continues, severe seasonal Influenza (flu) may happen alongside COVID-19. This could cause a "twindemic", in which there are additional burdens on health care resources and public safety compared to those occurring in the presence of a single infection. Amidst the raising trend of co-infections of the two diseases, forecasting both Influenza-like Illness (ILI) outbreaks and COVID-19 waves in a reliable and timely manner becomes more urgent than ever. Accurate and real-time joint prediction of the twindemic aids public health organizations and policymakers in adequate preparation and decision making. However, in the current pandemic, existing ILI and COVID-19 forecasting models face shortcomings under complex inter-disease dynamics, particularly due to the similarities in symptoms and healthcare-seeking patterns of the two diseases. METHODS: Inspired by the interconnection between ILI and COVID-19 activities, we combine related internet search and bi-disease time series information for the U.S. national level and state level forecasts. Our proposed ARGOX-Joint-Ensemble adopts a new ensemble framework that integrates ILI and COVID-19 disease forecasting models to pool the information between the two diseases and provide joint multi-resolution and multi-target predictions. Through a winner-takes-all ensemble fashion, our framework is able to adaptively select the most predictive COVID-19 or ILI signals. RESULTS: In the retrospective evaluation, our model steadily outperforms alternative benchmark methods, and remains competitive with other publicly available models in both point estimates and probabilistic predictions (including intervals). CONCLUSIONS: The success of our approach illustrates that pooling information between the ILI and COVID-19 leads to improved forecasting models than individual models for either of the disease.

Data from the internet enables the presence of infectious diseases such as influenza (flu) to be tracked and monitored. During the ongoing COVID-19 pandemic people will also be infected with flu, impacting health care providers. Predicting both COVID-19 and flu outbreaks in a timely manner enables health care providers and policymakers to prepare for the outbreaks. In this work, we develop a model to jointly predict cases of both COVID-19 and influenza-like illness that can be used at national and state levels in the USA. Our approach is more accurate than alternative similar approaches that predict cases of a single disease, showing the value of predicting the incidence of multiple diseases at the same time.

Searching for HIV and AIDS Health Information in South Africa, 2004-2019: Analysis of Google and Wikipedia Search Trends.

BACKGROUND: AIDS, caused by HIV, is a leading cause of mortality in Africa. HIV/AIDS is among the greatest public health challenges confronting health authorities, with South Africa having the greatest prevalence of the disease in the world. There is little research into how Africans meet their health information needs on HIV/AIDS online, and this research gap impacts programming and educational responses to the HIV/AIDS pandemic. OBJECTIVE: This paper reports on how, in general, interest in the search terms "HIV" and "AIDS" mirrors the increase in people living with HIV and the decline in AIDS cases in South Africa. METHODS: Data on search trends for HIV and AIDS for South Africa were found using the search terms "HIV" and "AIDS" (categories: health, web search) on Google Trends. This was compared with data on estimated adults and children living with HIV, and AIDS-related deaths in South Africa, from the Joint United Nations Programme on HIV/AIDS, and also with search interest in the topics "HIV" and "AIDS" on Wikipedia Afrikaans, the most developed local language Wikipedia service in South Africa. Nonparametric statistical tests were conducted to support the trends and associations identified in the data. RESULTS: Google Trends shows a statistically significant decline (P<.001) in search interest for AIDS relative to HIV in South Africa. This trend mirrors progress on the ground in South Africa and is significantly associated (P<.001) with a decline in AIDS-related deaths and people living longer with HIV. This trend was also replicated on Wikipedia Afrikaans, where there was a greater interest in HIV than AIDS. CONCLUSIONS: This statistically significant (P<.001) association between interest in the search terms "HIV" and "AIDS" in South Africa (2004-2019) and the number of people living with HIV and AIDS in the country (2004-2019) might be an indicator that multilateral efforts at combating HIV/AIDS-particularly through awareness raising and behavioral interventions in South Africa-are bearing fruit, and this is not only evident on the ground, but is also reflected in the online information seeking on the HIV/AIDS pandemic. We acknowledge the limitation that in studying the association between Google search interests on HIV/AIDS and cases/deaths, causal relationships should not be drawn due to the limitations of the data.

Use Internet search data to accurately track state level influenza epidemics.

For epidemics control and prevention, timely insights of potential hot spots are invaluable. Alternative to traditional epidemic surveillance, which often lags behind real time by weeks, big data from the Internet provide important information of the current epidemic trends. Here we present a methodology, ARGOX (Augmented Regression with GOogle data CROSS space), for accurate real-time tracking of state-level influenza epidemics in the United States. ARGOX combines Internet search data at the national, regional and state levels with traditional influenza surveillance data from the Centers for Disease Control and Prevention, and accounts for both the spatial correlation structure of state-level influenza activities and the evolution of people's Internet search pattern. ARGOX achieves on average 28% error reduction over the best alternative for real-time state-level influenza estimation for 2014 to 2020. ARGOX is robust and reliable and can be potentially applied to track county- and city-level influenza activity and other infectious diseases.

The landscape of cancer cell line metabolism.

Despite considerable efforts to identify cancer metabolic alterations that might unveil druggable vulnerabilities, systematic characterizations of metabolism as it relates to functional genomic features and associated dependencies remain uncommon. To further understand the metabolic diversity of cancer, we profiled 225 metabolites in 928 cell lines from more than 20 cancer types in the Cancer Cell Line Encyclopedia (CCLE) using liquid chromatography-mass spectrometry (LC-MS). This resource enables unbiased association analysis linking the cancer metabolome to genetic alterations, epigenetic features and gene dependencies. Additionally, by screening barcoded cell lines, we demonstrated that aberrant ASNS hypermethylation sensitizes subsets of gastric and hepatic cancers to asparaginase therapy. Finally, our analysis revealed distinct synthesis and secretion patterns of kynurenine, an immune-suppressive metabolite, in model cancer cell lines. Together, these findings and related methodology provide comprehensive resources that will help clarify the landscape of cancer metabolism.

Accurate regional influenza epidemics tracking using Internet search data.

Accurate, high-resolution tracking of influenza epidemics at the regional level helps public health agencies make informed and proactive decisions, especially in the face of outbreaks. Internet users' online searches offer great potential for the regional tracking of influenza. However, due to the complex data structure and reduced quality of Internet data at the regional level, few established methods provide satisfactory performance. In this article, we propose a novel method named ARGO2 (2-step Augmented Regression with GOogle data) that efficiently combines publicly available Google search data at different resolutions (national and regional) with traditional influenza surveillance data from the Centers for Disease Control and Prevention (CDC) for accurate, real-time regional tracking of influenza. ARGO2 gives very competitive performance across all US regions compared with available Internet-data-based regional influenza tracking methods, and it has achieved 30% error reduction over the best alternative method that we numerically tested for the period of March 2009 to March 2018. ARGO2 is reliable and robust, with the flexibility to incorporate additional information from other sources and resolutions, making it a powerful tool for regional influenza tracking, and potentially for tracking other social, economic, or public health events at the regional or local level.

An application of a Hill-based response surface model for a drug combination experiment on lung cancer.

Combination chemotherapy with multiple drugs has been widely applied to cancer treatment owing to enhanced efficacy and reduced drug resistance. For drug combination experiment analysis, response surface modeling has been commonly adopted. In this paper, we introduce a Hill-based global response surface model and provide an application of the model to a 512-run drug combination experiment with three chemicals, namely AG490, U0126, and indirubin-3 ' -monoxime (I-3-M), on lung cancer cells. The results demonstrate generally improved goodness of fit of our model from the traditional polynomial model, as well as the original Hill model on the basis of fixed-ratio drug combinations. We identify different dose-effect patterns between normal and cancer cells on the basis of our model, which indicates the potential effectiveness of the drug combination in cancer treatment. Meanwhile, drug interactions are analyzed both qualitatively and quantitatively. The distinct interaction patterns between U0126 and I-3-M on two types of cells uncovered by the model could be a further indicator of the efficacy of the drug combination.