Artificial Neural Network Language Models Predict Human Brain Responses to Language Even After a Developmentally Realistic Amount of Training.

Artificial neural networks have emerged as computationally plausible models of human language processing. A major criticism of these models is that the amount of training data they receive far exceeds that of humans during language learning. Here, we use two complementary approaches to ask how the models' ability to capture human fMRI responses to sentences is affected by the amount of training data. First, we evaluate GPT-2 models trained on 1 million, 10 million, 100 million, or 1 billion words against an fMRI benchmark. We consider the 100-million-word model to be developmentally plausible in terms of the amount of training data given that this amount is similar to what children are estimated to be exposed to during the first 10 years of life. Second, we test the performance of a GPT-2 model trained on a 9-billion-token dataset to reach state-of-the-art next-word prediction performance on the human benchmark at different stages during training. Across both approaches, we find that (i) the models trained on a developmentally plausible amount of data already achieve near-maximal performance in capturing fMRI responses to sentences. Further, (ii) lower perplexity-a measure of next-word prediction performance-is associated with stronger alignment with human data, suggesting that models that have received enough training to achieve sufficiently high next-word prediction performance also acquire representations of sentences that are predictive of human fMRI responses. In tandem, these findings establish that although some training is necessary for the models' predictive ability, a developmentally realistic amount of training (∼100 million words) may suffice.

Modelling the impact of human behavior using a two-layer Watts-Strogatz network for transmission and control of Mpox.

PURPOSE: This study aims to evaluate the effectiveness of mitigation strategies and analyze the impact of human behavior on the transmission of Mpox. The results can provide guidance to public health authorities on comprehensive prevention and control for the new Mpox virus strain in the Democratic Republic of Congo as of December 2023. METHODS: We develop a two-layer Watts-Strogatz network model. The basic reproduction number is calculated using the next-generation matrix approach. Markov chain Monte Carlo (MCMC) optimization algorithm is used to fit Mpox cases in Canada into the network model. Numerical simulations are used to assess the impact of mitigation strategies and human behavior on the final epidemic size. RESULTS: Our results show that the contact transmission rate of low-risk groups and susceptible humans increases when the contact transmission rate of high-risk groups and susceptible humans is controlled as the Mpox epidemic spreads. The contact transmission rate of high-risk groups after May 18, 2022, is approximately 20% lower than that before May 18, 2022. Our findings indicate a positive correlation between the basic reproduction number and the level of heterogeneity in human contacts, with the basic reproduction number estimated at 2.3475 (95% CI: 0.0749-6.9084). Reducing the average number of sexual contacts to two per week effectively reduces the reproduction number to below one. CONCLUSION: We need to pay attention to the re-emergence of the epidemics caused by low-risk groups when an outbreak dominated by high-risk groups is under control. Numerical simulations show that reducing the average number of sexual contacts to two per week is effective in slowing down the rapid spread of the epidemic. Our findings offer guidance for the public health authorities of the Democratic Republic of Congo in developing effective mitigation strategies.

The structure and behavioral patterns of the human population affected by ixodid tick bites in Irkutsk Region, Eastern Siberia, Russia.

The bites of hard ticks are the major route of transmission of tick-borne infections to humans, causing thousands of cases of diseases worldwide. However, the characteristics of the human population that is exposed to tick bites are still understudied. This work is aimed at characterizing both the structure of the population directly contacting ticks and the human behavioral features associated with tick bites. We studied 25,970 individuals who sought medical help after a tick bite at the Centre for Diagnostics and Prevention of Tick-borne Infections (CDPTBI) in Irkutsk City (Russian Federation). The demographic and behavioral characteristics of the human population were analyzed using z-tests for proportions, the Mann-Whitney U test, and the Spearman rank correlation coefficient. The majority of bitten people were urban residents (70 %), and most of them were either of active ages between 30 and 74 years old (62 %), or children between 0 and 9 years old (approximately 20%). Tick bites occurred mostly in the range of 150 km around the location of the diagnostic facility (83 %). In comparison to the general population, significant differences were revealed in the representation of different age groups among bitten people. The population affected by tick bites included fewer men and women in the ages of 10-29 and over 75 years old than would be predicted based on the demographics of the general population. Vice versa, the proportions of people in the ages of 5-9 and 60-74 increased among bitten people. Among men, such activities (in order of occurrence) as "leisure and recreation", "visiting allotments", "foraging for forest food", and "fulfilling work duties" tend to be more associated with tick bites. Among women, tick bites occurred mainly during "visiting allotments", "leisure and recreation", "visiting cemeteries" and "contact with pets and plants at home". The overall vaccination rate was 12 %; however, significantly more men than women were vaccinated against tick-borne encephalitis (up to 20 % vs. approximately 7 % respectively). The structure of the tick bite - affected population suggests that it is age-specific human behavior that mainly determines the frequency of contact between people and ticks. However, in several age groups, especially among children from 5 to 9 and people aged 30-39 years old, gender-related factors could significantly change the exposure of people to tick bites.

Fatigue and vigilance in medical experts detecting breast cancer.

An abundance of laboratory-based experiments has described a vigilance decrement of reducing accuracy to detect targets with time on task, but there are few real-world studies, none of which have previously controlled the environment to control for bias. We describe accuracy in clinical practice for 360 experts who examined >1 million women's mammograms for signs of cancer, whilst controlling for potential biases. The vigilance decrement pattern was not observed. Instead, test accuracy improved over time, through a reduction in false alarms and an increase in speed, with no significant change in sensitivity. The multiple-decision model explains why experts miss targets in low prevalence settings through a change in decision threshold and search quit threshold and propose it should be adapted to explain these observed patterns of accuracy with time on task. What is typically thought of as standard and robust research findings in controlled laboratory settings may not directly apply to real-world environments and instead large, controlled studies in relevant environments are needed.

Is the expression of different discrete emotions related to time? Evidence from online Chinese reviews using sentiment analysis and human behavior dynamics.

Objectives: The online behavior of online users has taken on complex and diverse characteristics, and posting product reviews on e-commerce platforms is no exception. In fact, reviews contain rich and multi-dimensional discrete emotional information, and whether there is a relationship between the expression of these different discrete emotions and the time interval between product purchase and review posting as well as their related characteristics are the issues that this study needs to analyze and solve in depth. Methods: Based on the OCC model (named after three proposers) of psychological emotional cognitive evaluation theory as the basis for emotion classification, the study used the massive amounts of Chinese reviews of mobile phones on the Chinese e-commerce platform Jingdong Mall as the research object, applied supervised machine learning methods to classify discrete emotions, and constructed a large corpus containing satisfaction, disappointment, admiration, reproach, love, and hate; then the study delved into the distribution and behavioral dynamics characteristics of consumers' comments containing the different discrete emotions at different "purchase-comment" time intervals. Results: The results showed that the first peak of the distribution curves of the six discrete emotions at different "purchase-comment" time intervals occurs on the first day after purchase and then decreases gradually but at different rates. The three curves for satisfaction, love, and hate emotions also show a second peak on the eleventh day, which is more similar to the bimodal distribution, implying that the corresponding product reviews are more objective. In addition, the distribution of reviews containing the six discrete emotions at different "purchase-comment" time intervals follows a power-law distribution and has the temporal characteristics of human behavioral dynamics, that is, "strong paroxysms and weak memory". However, the reviews containing the admiration and reproach emotions were most intensively written by consumers after the purchase, indicating that the service provided by the seller, logistics, and e-commerce platform stimulates more consumers to give quick responses and detailed reviews. Conclusion: This study is not only of great significance for exploring the internal mechanisms of consumer discrete emotional expression but also provides important decision-making references for potential consumer purchasing decisions, product updates for developers, marketing strategy formulation for marketing teams, and service improvement for sellers, logistics companies, and e-commerce platforms.

Early morning anopheline mosquito biting, a potential driver of malaria transmission in Busia County, western Kenya.

BACKGROUND: Insecticide-treated nets (ITNs) contributed significantly to the decline in malaria since 2000. Their protective efficacy depends not only on access, use, and net integrity, but also location of people within the home environment and mosquito biting profiles. Anopheline mosquito biting and human location data were integrated to identify potential gaps in protection and better understand malaria transmission dynamics in Busia County, western Kenya. METHODS: Direct observation of human activities and human landing catches (HLC) were performed hourly between 1700 to 0700 h. Household members were recorded as home or away; and, if at home, as indoors/outdoors, awake/asleep, and under a net or not. Aggregated data was analysed by weighting hourly anopheline biting activity with human location. Standard indicators of human-vector interaction were calculated using a Microsoft Excel template. RESULTS: There was no significant difference between indoor and outdoor biting for Anopheles gambiae sensu lato (s.l.) (RR = 0.82; 95% CI 0.65-1.03); significantly fewer Anopheles funestus were captured outdoors than indoors (RR = 0.41; 95% CI 0.25-0.66). Biting peaked before dawn and extended into early morning hours when people began to awake and perform routine activities, between 0400-0700 h for An. gambiae and 0300-0700 h for An. funestus. The study population away from home peaked at 1700-1800 h (58%), gradually decreased and remained constant at 10% throughout the night, before rising again to 40% by 0600-0700 h. When accounting for resident location, nearly all bites within the peri-domestic space (defined as inside household structures and surrounding outdoor spaces) occurred indoors for unprotected people (98%). Using an ITN while sleeping was estimated to prevent 79% and 82% of bites for An. gambiae and An. funestus, respectively. For an ITN user, most remaining exposure to bites occurred indoors in the hours before bed and early morning. CONCLUSION: While use of an ITN was estimated to prevent most vector bites in this context, results suggest gaps in protection, particularly in the early hours of the morning when biting peaks and many people are awake and active. Assessment of additional human exposure points, including outside of the peri-domestic setting, are needed to guide supplementary interventions for transmission reduction.

An evaluation of the impact of mitigation policies on health and the economy by managing social distancing during outbreaks.

The COVID-19 pandemic has necessitated various unavoidable social restrictions, leading to questions about the effectiveness of public emergency interventions and their impact economic growth. Block et al. (2020) conducted a notably study using an agent-based model to evaluate policies for reducing contact and demonstrated how choices in contact behavior can influence the rate and spread of the virus. However, their approach did not consider the economic consequences of these social restrictions. In response, we propose a set of strategies for governments to plan and evaluate policies during emergencies, aiming to contain infections while minimizing negative economic consequences. Our results indicate that there is no trade-off between containment strategies and economic output loss, making containment measures necessary policy instruments. However, potential trade-offs do emerge when selecting the most effective strategy. In this context, we propose and evaluate various policy alternatives to extreme "social distancing" measures, which can partially restore essential social interactions while preventing economic disasters induced by productivity losses.

Use, reuse, and waste management of single-use products associated with the COVID-19 pandemic in the United States.

Single-use product usage is not a new concern. However, during the early stages of the COVID-19 pandemic, the use and disposal of single-use products, especially those related to managing the pandemic, rose to prominence. Reports of shortages-and at the same time litter formation arising from improper disposal of various pandemic-related materials such as gloves, masks, wipes, and food takeout containers-were frequently relayed. To address shortages, it was recommended that single-use products be reused in some instances. As these recommendations were widely adopted, it became essential to assess consumer preferences regarding single-use product usage. Aiming to fill that void, a survey was distributed to learn about single-use product usage, possible reuse of single-use products, and waste-management practices during the COVID-19 pandemic in the US. Respondents preferred reusable fabric masks followed by disposable surgical masks. A significant percentage of respondents answered that they would reuse a disposable mask and mostly selected rotating masks as the preferred "disinfection" method in between the reuse of single-use masks. Gloves were not used by most respondents whereas wipes and/or paper towels were used by more than half of respondents. Free-response answers were analyzed for common themes. Concerns related to pandemic-related product use and disposal, and food packaging or food preparation were observed in the free-response answers. This survey reveals that respondents perceived changes in their consumption and waste generation or perceived a change in the type of products consumed and discarded due to the pandemic. Overall, respondents expressed a preference for reuse and a concern over the increase in single-use products. Results of this study can be used to make projections on the consumption and reuse of single-use products in crisis scenarios. In addition, the data can be used to model the use and disposal phase in single-use product life-cycle assessments. Integr Environ Assess Manag 2024;00:1-11. © 2024 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Mapping COVID-19's potential infection risk based on land use characteristics: A case study of commercial activities in two Egyptian cities.

The contagious COVID-19 has recently emerged and evolved into a world-threatening pandemic outbreak. After pursuing rigorous prophylactic measures two years ago, most activities globally reopened despite the emergence of lethal genetic strains. In this context, assessing and mapping activity characteristics-based hot spot regions facilitating infectious transmission is essential. Hence, our research question is: How can the potential hotspots of COVID-19 risk be defined intra-cities based on the spatial planning of commercial activity in particular? In our research, Zayed and October cities, Egypt, characterized by various commercial activities, were selected as testbeds. First, we analyzed each activity's spatial and morphological characteristics and potential infection risk based on the Centre for Disease Control and Prevention (CDCP) criteria and the Kriging Interpolation method. Then, using Google Mobility, previous reports, and semi-structured interviews, points of interest and population flow were defined and combined with the last step as interrelated horizontal layers for determining hotspots. A validation study compared the generated activity risk map, spatial COVID-19 cases, and land use distribution using logistic regression (LR) and Pearson coefficients (rxy). Through visual analytics, our findings indicate the central areas of both cities, including incompatible and concentrated commercial activities, have high-risk peaks (LR = 0.903, rxy = 0.78) despite the medium urban density of districts, indicating that urban density alone is insufficient for public health risk reduction. Health perspective-based spatial configuration of activities is advised as a risk assessment tool along with urban density for appropriate decision-making in shaping pandemic-resilient cities.

Identification and evaluation of educational technology trends from 2004 to 2022: Evidence based on computers in human behavior and horizon report.

The increasing influence of technology on education has attracted considerable attention. This study aims to determine the current status and development trends of educational technologies. At first, we used COOC, HistCite, and VOSviewer to systematically review 1562 educational articles published in Computers in Human Behavior (CHB) from 2004 to 2022. Based on bibliometrics, this study identified publication trends, research forces, collaboration, key articles, and research themes. Then, we visualized the technologies predicted by 30 Horizon Reports and combined them with CHB educational research to evaluate the accuracy of the identified trends. The results revealed an immediate influence of AI technology, extended reality and digital resources on education, a moderate influence of educational tools and games, and a delayed influence of data management and maker technology. In addition, human psychology and behavior in technological environment may be important themes in the future. In conclusion, this study not only proposes a comparative analysis of leading reports and representative literature, but also provides guidance for future research and development in educational technology.

Improving fraud detection with semi-supervised topic modeling and keyword integration.

Fraud detection through auditors' manual review of accounting and financial records has traditionally relied on human experience and intuition. However, replicating this task using technological tools has represented a challenge for information security researchers. Natural language processing techniques, such as topic modeling, have been explored to extract information and categorize large sets of documents. Topic modeling, such as latent Dirichlet allocation (LDA) or non-negative matrix factorization (NMF), has recently gained popularity for discovering thematic structures in text collections. However, unsupervised topic modeling may not always produce the best results for specific tasks, such as fraud detection. Therefore, in the present work, we propose to use semi-supervised topic modeling, which allows the incorporation of specific knowledge of the study domain through the use of keywords to learn latent topics related to fraud. By leveraging relevant keywords, our proposed approach aims to identify patterns related to the vertices of the fraud triangle theory, providing more consistent and interpretable results for fraud detection. The model's performance was evaluated by training with several datasets and testing it with another one that did not intervene in its training. The results showed efficient performance averages with a 7% increase in performance compared to a previous job. Overall, the study emphasizes the importance of deepening the analysis of fraud behaviors and proposing strategies to identify them proactively.

Modeling road user response timing in naturalistic traffic conflicts: A surprise-based framework.

There is currently no established method for evaluating human response timing across a range of naturalistic traffic conflict types. Traditional notions derived from controlled experiments, such as perception-response time, fail to account for the situation-dependency of human responses and offer no clear way to define the stimulus in many common traffic conflict scenarios. As a result, they are not well suited for application in naturalistic settings. We present a novel framework for measuring and modeling response times in naturalistic traffic conflicts applicable to automated driving systems as well as other traffic safety domains. The framework suggests that response timing must be understood relative to the subject's current (prior) belief and is always embedded in, and dependent on, the dynamically evolving situation. The response process is modeled as a belief update process driven by perceived violations to this prior belief, that is, by surprising stimuli. The framework resolves two key limitations with traditional notions of response time when applied in naturalistic scenarios: (1) The strong situation dependence of response timing and (2) how to unambiguously define the stimulus. Resolving these issues is a challenge that must be addressed by any response timing model intended to be applied in naturalistic traffic conflicts. We show how the framework can be implemented by means of a relatively simple heuristic model fit to naturalistic human response data from real crashes and near crashes from the SHRP2 dataset and discuss how it is, in principle, generalizable to any traffic conflict scenario. We also discuss how the response timing framework can be implemented computationally based on evidence accumulation enhanced by machine learning-based generative models and the information-theoretic concept of surprise.

Meta-analyses of motivational factors of response to natural disaster.

Response behavior of individuals is of critical importance to decrease chances of injury and death as well as ameliorate costs in property and infrastructure damage in natural disasters. Plenty of studies have examined which factors motivate individuals to respond to natural disasters. However, a systematic overview of the key motivating factors of various response behaviors is lacking. This study conducts a series of meta-analyses using data of 53,713 samples from 87 studies (77 papers) conducted in 27 different countries and regions to examine how 17 motivational factors were associated with individuals' response to natural disasters. The results indicate self-efficacy, outcome efficacy, attitudes, subjective norms, and information acquisition show the strongest effects on response behavior. Contrarily, the impact of negative affects like fear, depression, and anxiety on victims is minimal, despite the common assumption that they are significant related to response behaviour. In addition, current studies have disproportionally focused on studying risk perception, experience and information acquisition, earthquake and hurricane, and evacuation and preparation, while attention given to other types of motivational factors, disasters and response behaviors is lacking.

The effect of post-COVID-19 ventilation measures on indoor air quality in primary schools.

The recent COVID-19 pandemic has made people acutely aware of the importance of indoor air quality (IAQ) and building ventilation systems, particularly in densely occupied places like offices and schools. As a result, governments and other public entities are increasingly investing in the installation, maintenance, and upgrades of ventilation systems in public buildings. However, little is known about the effect of building ventilation systems on actual IAQ and its impact on occupant behavior. This paper exploits exogenous closing and opening events of schools during the COVID-19 pandemic, combined with policy measures focusing on maximizing ventilation rates inside classrooms, to assess the effectiveness of building ventilation systems in primary schools. We use a unique sensor network implemented before the COVID-19 pandemic, consisting of measurement devices installed in 252 classrooms across 27 Dutch primary schools, continuously monitoring IAQ indicators such as CO2 levels and fine particle concentrations. Using high-frequency data from 2018 to 2022 school years, we compare the IAQ differences between natural and mechanical ventilation through a fixed-effect identification strategy. Our results show that mechanically ventilated classrooms perform better with respect to CO2 and fine particle levels. However, the post-COVID-19 ventilation measures implemented after school reopening had stronger effects on naturally ventilated (NV) classrooms, suggesting behavioral changes at the classroom level. We also investigate the longer term effects of these post-COVID-19 ventilation measures and show some evidence of decay in effectiveness, as well as a strong seasonal effect, particularly in NV classrooms, which seems the result of a trade-off between ventilation and thermal comfort.

Drought as a trigger of the rapid rise of professional skateboarding in 1970s Southern California.

In 1977 California, authorities responded to an extreme drought with an unprecedented state order to drastically reduce domestic water usage and leave countless newly built swimming pools empty. These curved pools became "playgrounds" for inspired surfers to develop professional vertical skateboarding in the Los Angeles area. Industrial production of polyurethane, and the advent of digital photography, laser printing, and high gloss mass media further contributed to the explosive popularization of skateboarding, creating a global subculture and multibillion-dollar industry that still impacts music, fashion, and lifestyle worldwide. Our interdisciplinary investigation demonstrates that neither the timing nor the location of the origin of professional skateboarding was random. This modern case study highlights how environmental changes can affect human behavior, transform culture, and engender technical innovation in the Anthropocene.

Physiological Support of Goal-Directed Activity in Human.

The effectiveness of goal-directed human behavior and the processes underlying organization of such activity are the subjects of various biomedical studies. Here we review both classical and modern evidence on the fundamental principles of goal-directed human activity. Facts are presented about the basic mechanisms that ensure the effectiveness of goal-directed behavior and determine its physiological cost.

Editorial for Special Issue "Psychophysiology and Experimental Psychology".

The first experimental laboratory in psychology was founded in Leipzig (Germany), where Wilhelm Wundt mainly investigated feelings and sensations by employing experimental methods. Almost a century and half after is debut, experimental laboratories have extremely evolved in terms of apparatus, instruments, and recording techniques. Under a multiand interdisciplinary perspective, we can now better understand human cognitive and affective processes. As current zeitgeist has placed increasing emphasis upon the ecologically valid research, an "out-of-thelab" approach, integrated with both human and nonhuman research, is expected to leverage scientific advances in the field of human behavior.

El primer laboratorio experimental de psicología se fundó en Leipzig (Alemania), donde Wilhelm Wundt investigó principalmente los sentimientos y las sensaciones empleando métodos experimentales. Casi siglo y medio después de su debut, los laboratorios experimentales han evolucionado enormemente en cuanto a aparatos, instrumentos y técnicas de registro. Bajo una perspectiva multi e interdisciplinar, ahora podemos comprender mejor los procesos cognitivos y afectivos humanos. Dado que el zeitgeist actual ha puesto cada vez más énfasis en la investigación válida ecológicamente, se espera que un enfoque "fuera del laboratorio", integrado con la investigación humana y no humana, impulse los avances científicos en el campo del comportamiento humano.

Interaction of Human Behavioral Factors Shapes the Transmission of Arboviruses by Aedes and Culex Mosquitoes.

Arboviruses, i.e., viruses transmitted by blood-sucking arthropods, trigger significant global epidemics. Over the past 20 years, the frequency of the (re-)emergence of these pathogens, particularly those transmitted by Aedes and Culex mosquitoes, has dramatically increased. Therefore, understanding how human behavior is modulating population exposure to these viruses is of particular importance. This synthesis explores human behavioral factors driving human exposure to arboviruses, focusing on household surroundings, socio-economic status, human activities, and demographic factors. Household surroundings, such as the lack of water access, greatly influence the risk of arbovirus exposure by promoting mosquito breeding in stagnant water bodies. Socio-economic status, such as low income or low education, is correlated to an increased incidence of arboviral infections and exposure. Human activities, particularly those practiced outdoors, as well as geographical proximity to livestock rearing or crop cultivation, inadvertently provide favorable breeding environments for mosquito species, escalating the risk of virus exposure. However, the effects of demographic factors like age and gender can vary widely through space and time. While climate and environmental factors crucially impact vector development and viral replication, household surroundings, socio-economic status, human activities, and demographic factors are key drivers of arbovirus exposure. This article highlights that human behavior creates a complex interplay of factors influencing the risk of mosquito-borne virus exposure, operating at different temporal and spatial scales. To increase awareness among human populations, we must improve our understanding of these complex factors.

A multi-scale unified model of human mobility in urban agglomerations.

Understanding human mobility patterns is vital for the coordinated development of cities in urban agglomerations. Existing mobility models can capture single-scale travel behavior within or between cities, but the unified modeling of multi-scale human mobility in urban agglomerations is still analytically and computationally intractable. In this study, by simulating people's mental representations of physical space, we decompose and model the human travel choice process as a cascaded multi-class classification problem. Our multi-scale unified model, built upon cascaded deep neural networks, can predict human mobility in world-class urban agglomerations with thousands of regions. By incorporating individual memory features and population attractiveness features extracted by a graph generative adversarial network, our model can simultaneously predict multi-scale individual and population mobility patterns within urban agglomerations. Our model serves as an exemplar framework for reproducing universal-scale laws of human mobility across various spatial scales, providing vital decision support for urban settings of urban agglomerations.

Characterizing the role of human behavior in the effectiveness of contact-tracing applications.

Introduction: Although numerous countries relied on contact-tracing (CT) applications as an epidemic control measure against the COVID-19 pandemic, the debate around their effectiveness is still open. Most studies indicate that very high levels of adoption are required to stop disease progression, placing the main interest of policymakers in promoting app adherence. However, other factors of human behavior, like delays in adherence or heterogeneous compliance, are often disregarded. Methods: To characterize the impact of human behavior on the effectiveness of CT apps we propose a multilayer network model reflecting the co-evolution of an epidemic outbreak and the app adoption dynamics over a synthetic population generated from survey data. The model was initialized to produce epidemic outbreaks resembling the first wave of the COVID-19 pandemic and was used to explore the impact of different changes in behavioral features in peak incidence and maximal prevalence. Results: The results corroborate the relevance of the number of users for the effectiveness of CT apps but also highlight the need for early adoption and, at least, moderate levels of compliance, which are factors often not considered by most policymakers. Discussion: The insight obtained was used to identify a bottleneck in the implementation of several apps, such as the Spanish CT app, where we hypothesize that a simplification of the reporting system could result in increased effectiveness through a rise in the levels of compliance.