In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

Input: Consider Input: Introduction: Accurate and timely prediction for endemic infectious diseases is vital for public health agencies to plan and carry out any control methods at an early stage of disease outbreaks. Climatic variables has been identified as important predictors in models for infectious disease forecasts. Various approaches have been proposed in the literature to produce accurate and timely predictions and potentially improve public health response. Methods: We assessed how the machine learning LASSO method may be useful in providing useful forecasts for different pathogens in countries with different climates. Separate LASSO models were constructed for different disease/country/forecast window with different model complexity by including different sets of predictors to assess the importance of different predictors under various conditions. Results: There was a more apparent cyclicity for both climatic variables and incidence in regions further away from the equator. For most diseases, predictions made beyond 4 weeks ahead were increasingly discrepant from the actual scenario. Prediction models were more accurate in capturing the outbreak but less sensitive to predict the outbreak size. In different situations, climatic variables have different levels of importance in prediction accuracy. Conclusions: For LASSO models used for prediction, including different sets of predictors has varying effect in different situations. Short term predictions generally perform better than longer term predictions, suggesting public health agencies may need the capacity to respond at short-notice to early warnings.

Output: The utility of LASSO-based models for real time forecasts of endemic infectious diseases: A cross country comparison


Input: Consider Input: Purpose: Despite their controversial role, corticosteroids (CS) are frequently administered to patients with H1N1 virus infection with severe respiratory failure secondary to viral pneumonia. We hypothesized that invasive pulmonary aspergillosis (IPA) is a frequent complication in critically ill patients with H1N1 virus infection and that CS may contribute to this complication. Methods: We retrospectively selected all adult patients with confirmed H1N1 virus infection admitted to the intensive care unit (ICU) of two tertiary care hospitals from September 2009 to March 2011. Differences in baseline factors, risk factors, and outcome parameters were studied between patients with and without IPA. Results: Of 40 critically ill patients with confirmed H1N1, 9 (23 %) developed IPA 3 days after ICU admission. Five patients had proven and four had probable IPA. Significantly more IPA patients received CS within 7 days before ICU admission (78 versus 23 %, p = 0.002). IPA patients also received significantly higher doses of CS before ICU admission [hydrocortisone equivalent 800 (360-2,635) versus 0 (0-0) mg, p = 0.005]. On multivariate analysis, use of CS before ICU admission was independently associated with IPA [odds ratio (OR) 14.4 (2.0-101.6), p = 0.007]. Conclusions: IPA was diagnosed in 23 % of critically ill patients with H1N1 virus infection after a median of 3 days after ICU admission. Our data suggest that use of CS 7 days before ICU admission is an independent risk factor for fungal superinfection. These findings may have consequences for clinical practice as they point out the need for increased awareness of IPA, especially in those critically ill H1N1 patients already receiving CS.

Output: Invasive pulmonary aspergillosis is a frequent complication of critically ill H1N1 patients: a retrospective study


Input: Consider Input: Quickly detecting harmful cascades in networks can allow us to analyze the causes and prevent further spreading of destructive influence. Since it is often impossible to observe the state of all nodes in a network, a common method is to detect harmful cascades from sparsely placed sensors. However, the harmful cascades are usually dynamic (e.g., the cascade initiators and diffusion trajectories can change over the time), which can severely destroy the robustness of selected sensors. Meanwhile the large scale of current networks greatly increases the time complexity of sensor selection. Motivated by the observation, in this paper we investigate the scalable sensor selection problem for early detection of dynamic harmful cascades in networks. Specifically, we first put forward a dynamic susceptible-infected model to describe harmful cascades, and formally define a detection time minimization (DTM) problem which focuses on effective sensors placement for early detection of dynamic cascades. We prove that it is #P-hard to calculate the objective function exactly and propose two Monte-Carlo methods to estimate it efficiently. We prove the NP-hardness of DTM problem and design a corresponding greedy algorithm. Based on that, we propose an efficient upper bound based greedy (UBG) algorithm with the theoretical performance guarantee reserved. To further meet different types of large-scale networks, we propose two accelerations of UBG: Quickest-Path-UBG for sparse networks and Local-Reduction-UBG for dense networks to improve the time complexity. The experimental results on synthetic and real-world social networks demonstrate the practicality of our approaches.


Output: Early detection of dynamic harmful cascades in large-scale networks ଝ