COVID-19 and armed conflict.

This article studies the impact of COVID-19 on armed conflict. The pandemic has significant health, economic and political effects. These can change the grievances and opportunity structures relevant for armed conflicts to either increase or decrease conflict risks. I analyse empirical evidence from Afghanistan, Colombia, India, Iraq, Libya, Pakistan, the Philippines, Thailand and Yemen from the first six months of 2020. Results suggest that COVID-19 provides little opportunities for health diplomacy and cooperation, but it also has not yet driven grievances to a level where they became relevant for armed conflicts. Four countries have encountered temporary declines in armed conflicts, mostly due to strategic decisions by governments or rebels to account for impeded logistics and to increase their popular support. Armed conflict levels have increased in five countries, with conflict parties exploiting either state weakness or a lack of (international) attention due to the COVID-19 pandemic. This is a worrisome trend given the tremendous impacts of armed conflict on human security and on the capabilities of countries to deal with health emergencies.

Nurses' Protests during the COVID-19 Pandemic: A Comparative International Analysis.

Nurses play key roles in dealing with pandemics yet are often conceived solely as "technical" experts without political agency. This study conducts the first global comparative analysis of COVID-19-related protests of nurses and other frontline health workers, with a focus on the first 18 months of the pandemic. We draw on quantitative and qualitative data on nurses' protests and protest drivers. Results show that such protests were widespread: We identify 3515 events in 90 countries, with several regional hotspots existing. The most common reasons for protests were poor working conditions and insufficient workplace safety, followed by wider social issues like poverty and racism. For most of the time period under consideration, protests demanding access to vaccinations (a rarely explored phenomenon) were more widespread than anti-vaccination events. Protest frequency was highest in countries with high COVID-19-related mortality rates, high levels of human development, and strong social movements at the onset of the pandemic. Recognising the key role of nurses as political actors would help to improve health policies and to maintain a capable healthcare workforce, particularly during acute crises like pandemics.

Collective Violence against Health Workers in the Context of the COVID-19 Pandemic.

Concerns about violence against nurses and other medical personnel have increased during the COVID-19 pandemic. However, as of yet, limited systematic knowledge of such violence is available. Addressing this gap, we analyse the geographical distribution of, motivations behind, and contexts of collective attacks against health workers in the context of the COVID-19 pandemic. To do so, we systematically recorded and coded attack events worldwide from 1 March 2020 to 31 December 2021. We identify high-risk countries, attack characteristics, and the socio-economic contexts in which attacks tend to occur. Our results show that opposition against public health measures (28.5%), fears of infection (22.3%), and supposed lack of care (20.6%) were the most common reasons for attacks. Most attacks occurred in facilities (often related to a supposed lack of care) or while health workers were on duty in a public place (often due to opposition to public health measures). However, 17.9% of all attacks took place in off-duty settings. Democratic countries with high vaccination rates and strong health systems were relatively safe for nurses and doctors. Distrust in the skills of health workers and the science underlying health interventions is a major driver of collective attack risks and should be addressed before it turns violent. This study was not registered.

An integrated deep-learning and multi-level framework for understanding the behavior of terrorist groups.

Human security is threatened by terrorism in the 21st century. A rapidly growing field of study aims to understand terrorist attack patterns for counter-terrorism policies. Existing research aimed at predicting terrorism from a single perspective, typically employing only background contextual information or past attacks of terrorist groups, has reached its limits. Here, we propose an integrated deep-learning framework that incorporates the background context of past attacked locations, social networks, and past actions of individual terrorist groups to discover the behavior patterns of terrorist groups. The results show that our framework outperforms the conventional base model at different spatio-temporal resolutions. Further, our model can project future targets of active terrorist groups to identify high-risk areas and offer other attack-related information in sequence for a specific terrorist group. Our findings highlight that the combination of a deep-learning approach and multi-scalar data can provide groundbreaking insights into terrorism and other organized violent crimes.

Exploring the worldwide impact of COVID-19 on conflict risk under climate change.

Objectives: Understand whether and how the COVID-19 pandemic affects the risk of different types of conflict worldwide in the context of climate change. Methodology: Based on the database of armed conflict, COVID-19, detailed climate, and non-climate data covering the period 2020-2021, we applied Structural Equation Modeling specifically to reorganize the links between climate, COVID-19, and conflict risk. Moreover, we used the Boosted Regression Tree method to simulate conflict risk under the influence of multiple factors. Findings: The transmission risk of COVID-19 seems to decrease as the temperature rises. Additionally, COVID-19 has a substantial worldwide impact on conflict risk, albeit regional and conflict risk variations exist. Moreover, when testing a one-month lagged effect, we find consistency across regions, indicating a positive influence of COVID-19 on demonstrations (protests and riots) and a negative relationship with non-state and violent conflict risk. Conclusion: COVID-19 has a complex effect on conflict risk worldwide under climate change. Implications: Laying the theoretical foundation of how COVID-19 affects conflict risk and providing some inspiration for the implementation of relevant policies.

Modelling armed conflict risk under climate change with machine learning and time-series data.

Understanding the risk of armed conflict is essential for promoting peace. Although the relationship between climate variability and armed conflict has been studied by the research community for decades with quantitative and qualitative methods at different spatial and temporal scales, causal linkages at a global scale remain poorly understood. Here we adopt a quantitative modelling framework based on machine learning to infer potential causal linkages from high-frequency time-series data and simulate the risk of armed conflict worldwide from 2000-2015. Our results reveal that the risk of armed conflict is primarily influenced by stable background contexts with complex patterns, followed by climate deviations related covariates. The inferred patterns show that positive temperature deviations or precipitation extremes are associated with increased risk of armed conflict worldwide. Our findings indicate that a better understanding of climate-conflict linkages at the global scale enhances the spatiotemporal modelling capacity for the risk of armed conflict.