Campaign Planning Research Articles

Challenges in Influenza Control and Surveillance in the Republic of Kazakhstan

The COVID-19 pandemic has significantly disrupted the circulation of influenza viruses in Kazakhstan, highlighting the vulnerabilities in the country’s public health infrastructure. This review critically examines the challenges faced in infiltrating and controlling influenza in Kazakhstan, particularly in light of the shifting epidemiological landscape post-pandemic. Key issues include the decline in influenza cases during the pandemic, which complicates the assessment of influenza epidemiology, vaccine effectiveness, and planning of vaccination campaigns. Although part of the Global Influenza Hospital Surveillance Network (GIHSN), Kazakhstan's surveillance systems face data collection, coordination, and public awareness gaps. The review discusses the prevalence of various influenza strains, the impact of zoonotic infections, and the necessity for improved monitoring frameworks. Additionally, the historical context of infectious disease control in Kazakhstan is explored, emphasising the need for enhanced international collaboration and targeted public health strategies. The findings underscore the importance of vaccination and robust surveillance to mitigate the risks of seasonal and pandemic influenza, advocating for a comprehensive approach to safeguard public health in Kazakhstan.

Delivering insecticide-treated nets (ITNs) through a digitized single-phase door-to-door strategy: lessons from Ondo state, Nigeria

BackgroundThe use of insecticide-treated nets (ITNs) is a strategy recommended by the World Health Organization (WHO) for malaria prevention. In Nigeria, ITNs have been periodically distributed since 2007 through campaigns. Campaign activities and assets are typically tracked using either a paper-based or digital system. In 2017, a digital approach was introduced in Ondo state for tracking attendance at training sessions as part of the ITN campaign. Following the success of the 2017 introduction, subsequent campaigns planned to digitise other aspects of the campaign to improve accountability and efficiency of the ITN distribution. The COVID-19 pandemic posed additional challenges for the ITN distribution planned for 2021 and adaptations were made to the programme strategy to ensure the campaign could go ahead safely. This article presents lessons and experiences from the 2021 ITN distribution campaign in Ondo state, Nigeria.MethodsThe campaign used RedRose, a customised mobile application, to monitor the planning and delivery of the campaign, collect household information including training personnel and tracking the transfer of ITNs between distribution hubs and households. ITNs were delivered through a single-phase door-to-door distribution strategy.ResultsThe campaign distributed 2,965,125 ITNs covering 1,057,577 households across Ondo state. The digital application was beneficial for monitoring the quality of implementation and tracking assets and staff to ensure safety.. The single-phase door-to-door approach was more convenient for households compared to fixed-point distribution but increased the workload for mobilization and distribution teams.ConclusionsSingle phase door-to-door strategy using digital tools was an effective method to increase coverage of ITNs while closely tracking the progress of distribution campaigns. High-quality population data are needed to further improve the planning and implementation of ITN campaigns and other health interventions.

Patterns of animal rabies in the Nizhny Novgorod region of Russia (2012-2022): the analysis of risk factors.

Animal rabies is a viral disease that poses a significant threat to domestic and wild animal populations, with devastating consequences for animal health and human life. Understanding and assessing the risk factors associated with the transmission and persistence of the rabies virus in wild and domestic animal populations is crucial for developing effective strategies to control and mitigate cases. Studies of the spatial and temporal distribution of rabies cases in the Nizhny Novgorod region during 2012-2022 provided epidemiological evidence of the circulation of infection between animals in the presence of vaccination. Among the wild animals in the area, red foxes play a major role in the spread of rabies, accounting for 96.4% of all wild animal cases. We used spatiotemporal cluster analysis and a negative binomial regression algorithm to study the relationships between animal rabies burden by municipality and a series of environmental and sociodemographic factors. The spatiotemporal cluster analysis suggests the concentration of wild animal rabies cases in the areas of high fox population density and insufficient vaccination rates. The regression models showed satisfactory performance in explaining the observed distribution of rabies in different animals (R 2 = 0.71, 0.76, and 0.79 in the models for wild, domestic and all animals respectively), with rabies vaccination coverage and fox population density being among the main risk factors. We believe that this study can provide valuable information for a better understanding of the geographical and temporal patterns of rabies distribution in different animal species, and will provide a basis for the development of density-dependent planning of vaccination campaigns.

An observational analysis of the impact of deltamethrin + piperonyl butoxide insecticide-treated nets on malaria case incidence and entomological indicators in Ebonyi State, Nigeria, 2017–2021

BackgroundIntense pyrethroid resistance threatens the effectiveness of the primary vector control intervention, insecticide-treated nets (ITNs), in Nigeria, the country with the largest malaria burden globally. In this study, the epidemiological and entomological impact of a new type of ITN (piperonyl-butoxide [PBO] ITNs) distributed in Ebonyi State were evaluated. The epidemiological impact was also compared to the impact of standard pyrethroid-only ITNs in Cross River State.MethodsA controlled interrupted time series analysis was conducted on monthly malaria incidence data collected at the health facility level, using a multilevel mixed-effects negative binomial model. Data were analysed two years before and after the PBO ITN campaign in Ebonyi State (December 2017 to November 2021). A pre-post analysis, with no comparison group, was used to assess the impact of PBO ITNs on human biting rates and indoor resting density in Ebonyi during the high transmission season immediately before and after the PBO ITN campaign.ResultsIn Ebonyi, PBO ITNs were associated with a 46.7% decrease (95%CI: -51.5, -40.8%; p < 0.001) in malaria case incidence in the 2 years after the PBO ITN distribution compared to a modelled scenario of no ITNs distributed, with a significant decrease from 269.6 predicted cases per 1000 population to 143.6. In Cross River, there was a significant 28.6% increase (95%CI: -10.4, 49.1%; p < 0.001) in malaria case incidence following the standard ITN distribution, with an increase from 71.2 predicted cases per 1000 population to 91.6. In Ebonyi, the human biting rate was 72% lower (IRR: 0.28; 95%CI 0.21, 0.39; p < 0.001) and indoor resting density was 73% lower (IRR: 0.27; 95%CI 0.21, 0.35; p < 0.001) after the PBO ITNs were distributed.ConclusionsThe epidemiological and entomological impact of the PBO ITNs underscore the impact of these ITNs in areas with confirmed pyrethroid resistance. These findings contribute to ongoing research on the impact of new types of ITNs in Nigeria, providing critical evidence for the Nigeria National Malaria Elimination Programme and other countries for future ITN procurement decisions as part of mass ITN campaign planning and malaria programming.

Applying a new theoretical and methodological approach for behavior-change campaign planning: identifying the critical determinants for reducing littering and evaluating the resulting large-scale campaign.

This paper presents a theoretical concept and methodological approach for identifying critical determinants for behavior change interventions. The approach is based on established theories and constructs but represents them in an intervention- instead of questionnaire-oriented form. Six discriminant and targetable dimensions of behavior determinants are proposed: Consideration, feasibility, instrumental evaluation, norms and goals, affective evaluation, and needs and tension states. For estimating the importance of these dimensions for a specific behavior to be changed in a specific situation and population, a quasi-experimental approach is proposed, in which interventions are designed to have effects on one of these dimensions but none on the other dimensions. By measuring changes of the target behavior or its consequences, the impact of each dimension on changing the behavior can be estimated in-situ without questionnaires. The approach was applied to develop a campaign for reducing picnic littering in an urban park in Zurich (Switzerland). In 2019, posters targeting four dimensions were set up during three waves in up to four zones with two control zones without posters. Before, between, and after the intervention waves, for at least 2 weeks, no interventions were in place. The volume of litter was measured on 119 days at 55 points. In some cases, the amount of litter was too small for effects to be detected, but where enough littering occurred, posters providing information, inducing positive emotions, or activating reciprocity norms-as well as providing the option of separating fractions of waste for recycling as a structural measure-reduced litter significantly. Interventions targeting the tension state of disgust had no effect. Posters targeting descriptive and injunctive norms increased the amount of litter. Based on the results of the preparative study, a large-scale campaign was designed, implemented, and evaluated in 2022, which led to promising effects.

Hantavirus Pulmonary Syndrome Outbreak Anticipation by a Rapid Synchronous Increase in Rodent Abundance in the Northwestern Argentina Endemic Region: Towards an Early Warning System for Disease Based on Climate and Rodent Surveillance Data.

Hantavirus pulmonary syndrome (HPS) is an American emerging disease caused by the rodent-borne virus genus Orthohantavirus (Family: Hantaviridae: Order: Elliovirales Class: Bunyaviricetes). In Argentina, almost half of the HPS infections occur in the northwestern endemic region. In this study, we monitored rodent abundance during 2022 and 2023 in three sites with different sampling methods (removal trapping, live trapping and hunted rodents by domestic cats) to evaluate their relationship with human infections. We found a similar pattern of variation in rodent abundance across time, and particularly a synchronous rise of rodent abundance that anticipated an HPS outbreak in 2023. Our dynamic regression models revealed a positive relationship between HPS cases and rodent abundance with a three-month lag, as well as rainfall with an eight-month lag. Our results provide a framework for the planning and implementation of public health prevention campaigns based on climatology and rodent monitoring. Domestic cats bringing rodents into houses can be an overlooked risk factor, particularly if viral shedding of infected rodents is magnified by stress. HPS is a disease of public health concern due to its high mortality rate, the lack of a specific therapeutic treatment and no vaccine. Thus, prevention of infections is of the utmost importance.

The prediction model for advertising campaigns in IPTV networks based on the possibilities of displaying geographically targeted messages

Abstract Subject and purpose of work This paper presents a novel predictive model for advertising campaigns in IPTV networks that uses the advanced geolocation-based display of advertising messages. Thanks to advanced data analysis and modern geolocation technologies, the proposed model enables the more precise planning and optimisation of advertising campaigns. Advertisers can thus better understand the preferences and behaviours of audiences in different geographical regions. This allows the creation of more tailored and engaging content of programmes and advertisements, leading to higher levels of viewer engagement in the content presented and increasing the effectiveness of campaigns. Materials and methods For the purposes of the project, data from the IPTV advertising campaign covering from 50,000 to 500,000 displays between 23rd January 2024 and 20th March 2024 were used. The TERYT database and the list of counties were used to increase the precision of target group targeting. Viewer rating and interaction data were collected and analysed to assess the effectiveness of the campaign. The methodology included monitoring the number of channel switches, advertising message views, complete views, the average display time of the advertising message and unique reach in different time periods. Results The results of the test of the advertising campaign based on switching channels using a remote control show that increasing the number of ad plays clearly improves the effectiveness of reaching the audience. In the beginning, with fewer displays, the chances of an ad being noticed by viewers are moderate. However, as the number of displays increases, these chances increase exponentially, reaching nearly 45% at 500,000 ad plays.The analysis of these results is very important for the effective planning of advertising campaigns. Thanks to these data, advertisers can determine more accurately the number of displays that needs to be planned in order to achieve their marketing goals. The significant increase in the likelihood of reaching the audience with a bigger number of displays indicates that investing in additional views can be cost-effective and lead to the better use of the advertising budget. Conclusions The project confirmed that precise targeting and optimisation of the number of displays in IPTV advertising campaigns significantly increase the effectiveness of the advertisements. The high rate of complete views and the wide reach of the advertisements attest to the effectiveness of the strategy used. The test results show that smart campaign planning in IPTV can lead to better audience engagement and the more efficient use of the advertising budget. These findings suggest that IPTV has great potential as a modern advertising medium, offering advertisers the tools to effectively reach specific target groups and increase their engagement.

Geomorphological evidence for volcano-tectonic deformation along the unstable western flank of Cumbre Vieja Volcano (La Palma)

In 2021, La Palma's southern volcanic complex Cumbre Vieja erupted for its longest period in historic times. Although the geological record shows no evidence for a collapse of Cumbre Vieja, ground deformation studies and field observations suggest that its western flank is moving seawards, following the direction of previous collapses of the island. To better estimate the hazard of a potential flank collapse of Cumbre Vieja, it is important to identify the lateral extent and depth of the mobile sector. Here, we analyse the volcano-tectonic deformation along Cumbre Vieja's western flank, based on geomorphological analysis of combined topographic and new ship-born bathymetric data as well as the analysis of shallow seismicity records associated with the 2021 eruption. In our interpretation, the shoreline-crossing Puerto Naos Ridge results from tectonic uplift accompanying transpressional deformation along the northern boundary of Cumbre Vieja's moving flank, therefore decoupling a stable sector in the north from the mobile sector farther south. The proposed moving sector is consistent in scale with previous ground deformation studies and documented flank collapses of structurally similar volcanoes. We present a workflow for semi-automatically detecting boundaries of unstable volcanic flanks based on morphological changes captured in digital elevation data. The method correctly delineated the known boundaries of the unstable flanks of Mt. Etna and Kilauea volcanoes. The ability to constrain potential boundaries of unstable volcanic flanks should inform the planning of future geophysical and geodetic campaigns aiming to identify precursory signals of potential flank failures.

Stroke awareness in a Brazilian Northeastern capital city and the burden of the COVID-19 pandemic.

The delay in recognizing stroke symptoms is a significant obstacle to receiving acute treatment. Therefore, it is essential to understand the gaps in the knowledge about stroke among the general population and promote campaigns based on these gaps. To investigate the general knowledge about stroke in a capital in Northeastern Brazil in a sample of individuals who attended a public hospital and the impacts of the coronavirus disease 2019 (COVID-19) pandemic on the use of emergency services. We included patients older than 18 years of age and their family members and/or companions. After obtaining consent, the researcher presented a typical case of stroke, and the participants filled out a questionnaire divided into 2 sections: sociodemographic data and 15 questions about stroke detection and seeking health services and treatment. We included 154 individuals with a mean age of 44.45 ± 16.21 years. After presenting the case, 60.4% mentioned the acronym AVC (acidente vascular cerebral, or cerebrovascular accident [stroke], in Portuguese) as a possible explanation, and 54.5% reported that they would call the Mobile Emergency Care Service. However, 62.9% provided the incorrect telephone number for the Mobile Emergency Care Service or lacked knowledge of the accurate number. Regarding the risk factors for stroke, 27.9% did not know any of them, 65.5% were unaware of any treatment, and no reference was made to thrombolytic therapy. About their chosen conduct in the same case in the context of the COVID-19 pandemic, 98.1% of the participants would not change their behavior. These results can assist in the planning of public policies and campaigns emphasizing the issue of risk factors and how to access emergency medical services in the state of Alagoas, Brazil.

Integrating Genomic, Climatic, and Immunological Factors to Analyze Seasonal Patterns of Influenza Variants

Influenza, often referred to as the flu, is an extremely contagious respiratory illness caused by influenza viruses, impacting populations globally with significant health consequences annually. A hallmark of influenza is its seasonal patterns, influenced by a mix of geographic, evolutionary, immunological, and environmental factors. Understanding these seasonal trends is crucial for informing public health decisions, including the planning of vaccination campaigns and their formulation. In our study, we introduce a genotype-structured infectious disease model for influenza transmission, immunity, and evolution. In this model, the population of infected individuals is structured according to the virus they harbor. It considers a symmetrical fitness landscape where the influenza A and B variants are considered. The model incorporates the effects of population immunity, climate, and epidemic heterogeneity, which makes it suitable for investigating influenza seasonal dynamics. We parameterize the model to the genomic surveillance data of flu in the US and use numerical simulations to elucidate the scenarios that result in the alternating or consecutive prevalence of flu variants. We show that the speed of virus evolution determines the alternation and co-circulation patterns of seasonal influenza. Our simulations indicate that slow immune waning reduces how often variants change, while cross-immunity regulates the co-circulation of variants. The framework can be used to predict the composition of future influenza outbreaks and guide the development of cocktail vaccines and antivirals that mitigate influenza in both the short and long term.

Chasing parts in quadrillion: applications of dynamical downscaling in atmospheric pollutant transport modelling during field campaigns

Atmospheric transport and dispersion models (ATDMs) are widely used to study and forecast pollution events. In the frame of the “Effect of Megacities on the transport and transformation of pollutants on the regional to global scales” (EMeRGe) project, ATDM forecasts were carried out to identify potential airborne sampling areas of perfluorocarbons (PFCs) emanating from controlled PFC releases. The forecasts involved short-distance transport over small-scale topographic maxima (Manilla; Philippines), short-distance transport over large-scale topographic maxima (Taipei, Taiwan) and long-distance transport over mixed topography (Nanjing, China, sampled over Taiwan). In situ aircraft measurements of PFC mixing ratios down to a few parts per quadrillion (ppqv) provide us with a unique dataset to explore the added benefits of dynamical downscaling. Transport simulations were repeated using FLEXPART driven by ERA5 and IFS meteorological data and FLEXPART-WRF with dynamically downscaled IFS data down to 1.1 km and four PBL parametrisations. Of the three cases studied, dynamical downscaling led to significant differences for the Manilla and Taipei releases that can be interpreted through changes in the modelled orographic flow regimes. The choice of PBL scheme also significantly impacted accuracy, but there was no systematically better-performing option, highlighting the benefits of ensemble forecasting. Results show how convergence and divergence between ensemble members can be utilised to help decision-making during field campaigns. This study highlights the role that dynamical downscaling can play as an important component in campaign planning when dealing with observations over orographically complex areas.

Combustion behavior of NMP-001, a nitromethane-based green rocket propellant

Hydrazine’s carcinogenic and toxic nature makes its use in space propulsion expensive and necessitates careful launch campaign planning, which may cause delays. One already established alternative is to use energetic salt-based ionic solution propellants. However, the costs remain high because these salts are costly and highly regulated energetic materials. Additionally, propellants based on these energetic salts produce much higher temperatures during decomposition and combustion, as is the case for hydrazine. Consequently, expensive refractory metal-based alloys and high-temperature oxidation-resistant catalytic reactors must be used, which further drives up the costs. Alternatively, hydrogen peroxide may be used, though only in applications with a low specific impulse requirement. This study explores an alternative approach: using nitromethane, a commonly available laboratory solvent. NMP-001 is a nitromethane-based monopropellant that contains additives that allow for low-pressure combustion and reduce sensitivity to mechanical stimuli. Multiple combustion chamber configurations with varying characteristic lengths were tested in rocket combustion experiments. With the 11.5 m L∗-configuration, a 15.1 bar low-pressure limit could be determined. The 7.3 and 5.0 m L∗-configurations yielded a 16.2 and 28.3 bar low-pressure combustion limits, respectively. These outcomes are significantly below the 30 bar limit reported in the literature. Despite using only a simple heat-sink combustion chamber with a non-sophisticated injector element and a non-optimized propellant formulation, combustion was 83 to 95 % efficient with a C* ranged between 1133 to 1302 m/s. These figures are higher than for hydrogen peroxide and are comparable to the theoretical performance of state-of-the-art green liquid monopropellants.

Factors influencing canine rabies vaccination among dog-owning households in Nigeria

Rabies perpetuates in Nigeria despite initiatives like the Regional Disease Surveillance System Enhancement Project, with evidence indicating suboptimal canine vaccination rates as a contributing factor. To inform effective planning of mass dog vaccination campaigns, it is crucial to understand the factors associated with variation in canine vaccination rates. We conducted a cross-sectional study in 2022 to understand factors associated with canine vaccination. We used stratified random sampling of the streets and dog-owning households to survey 4162 households from three states and the Federal Capital Territory (FCT). We then built a joint probabilistic model to understand factors associated with dog vaccination and non-vaccination. First, we modelled rabies knowledge as a latent variable indirectly measured with several targeted survey questions. This method allowed a respondent's unobserved understanding of rabies to be estimated using their responses to a collection of survey questions that targeted different aspects of rabies epidemiology and took various possible response distributions (i.e., ordinal, categorical, binary). Second, we modelled factors influencing pet owners' decisions to vaccinate their dogs against rabies and barriers to dog vaccination among dog owners whose dogs were not vaccinated against rabies. Posterior distributions revealed that the probability of dog vaccination was positively associated with the owner's latent knowledge of rabies, civil servant service employment, residence in the FCT, ownership of a single dog, providing care to dogs, and a preference for contemporary treatment following a dog bite. Conversely, non-vaccination was positively associated with private employment, residing in Anambra and Enugu states, owning multiple dogs, allowing dogs to search for leftovers, and a preference for traditional treatment after a dog bite. Cost was the primary barrier against vaccination for dog owners in Anambra and Enugu, while mistrust posed a major challenge for those in the FCT. Owners in areas with veterinary establishments cited cost as a barrier, while those without a veterinary establishment cited access as the primary barrier. Our study underscores the need to enhance rabies knowledge, tailor vaccination campaigns to specific demographics, address financial and access barriers, and combat hesitancy to improve rabies vaccination rates in Nigeria.

A pre-post evaluation study of a social media-based COVID-19 communication campaign to improve attitudes and behaviors toward COVID-19 vaccination in Tanzania.

In Tanzania, the One by One: Target COVID-19 campaign was launched nationally in July 2022 to address the prevalent vaccine hesitancy and lack of confidence in COVID-19 vaccines. The campaign mobilized social media influencers and viral content with the ultimate goal of increasing COVID-19 vaccine uptake in the country. The objective of this study was to empirically assess the impact of the campaign on three outcomes: vaccine confidence, vaccine hesitancy, and vaccination status. Using programmatic data collected through an online survey before and after the campaign, we conducted a difference-in-difference (DiD) analysis and performed a crude, adjusted, and propensity score-matched analysis for each study outcome. Lastly, to observe whether there was any differential impact of the campaign across age groups, we repeated the analyses on age-stratified subgroups. Data included 5,804 survey responses, with 3,442 and 2,362 responses collected before and after the campaign, respectively. Although there was only weak evidence of increased COVID-19 vaccine confidence in the campaign-exposed group compared to the control group across all age groups, we observed a differential impact among different age groups. While no significant change was observed among young adults aged 18-24 years, the campaign exposure led to a statistically significant increase in vaccine confidence (weighted/adjusted DiD coefficient = 0.76; 95% CI: 0.06, 1.5; p-value = 0.034) and vaccination uptake (weighted/adjusted DiD coefficient = 1.69.; 95% CI: 1.02, 2.81; p-value = 0.023) among young adults aged 25-34 years. Among adults aged 35 years and above, the campaign exposure led to a significant decrease in vaccine hesitancy (weighted/adjusted DiD coefficient = -15; 95% CI: -21, -8.3; p-value<0.001). The social media campaign successfully improved vaccine hesitancy, confidence, and uptake in the Tanzanian population, albeit to varying degrees across age groups. Our study provides valuable insights for the planning and evaluation of similar social media communication campaigns aiming to bolster vaccination efforts.

Mass media campaigns and the 'file drawer problem': A mixed methods study of how to avoid campaign failure.

Mass media campaigns are frequently used to address public health issues. Considering the considerable cost, there has been little analysis of why campaigns sometimes fail. This study used a sequential mixed methods approach to explore the mechanisms that can lead to failure and to identify what can be done to avoid or overcome common mistakes in campaign planning, implementation, and evaluation. We conducted interviews and a survey with 28 public health social marketing and mass media campaign experts over three rounds of research and analysed the data thematically, generating themes inductively. We identified four systemic factors that drive success: long-term strategic thinking and commitment, understanding the campaign context, doing and learning from evaluation, and fostering strong relationships. The factors did not operate in isolation, rather good (or poor) execution in one area was likely to influence performance in another. The experts also emphasised that a change of political context could drastically affect one or more of the identified factors. Our analysis showed that campaign failures are not simply individuals making mistakes. Systemic issues throughout the planning, execution, and evaluation phases need to be addressed if campaign outcomes are to improve.

Research on Seasonal Orderliness and Linear Correlation of Vegetable Sales Data

The key to the study of seasonal orderliness and linear correlation of vegetable sales data is to understand how sales patterns are affected by seasonal factors and the linear relationship between these patterns and other variables. Seasonal ordering reflects cyclical changes in sales data over time that are influenced by factors such as climate, traditional festivals, and consumption habits. For example, certain vegetables may sell better in the winter while others in the summer. By analysing historical sales data, time series models such as Autoregressive Moving Average (ARMA) can be used to forecast future sales trends, which in turn support inventory management and pricing strategies. In addition, linear correlation analysis helps to identify the relationship between sales volume and factors such as price, cost and market demand, as well as the interactions between different vegetables, including complementary and substitution effects. This information is crucial for supply chain optimisation, promotional campaign planning and risk management. With the development of big data and analytics, researchers can more accurately model and predict market behaviours to support strategic decision-making for vegetable producers and retailers. Research that integrates seasonal ordering and linear correlation will provide insights into the complex dynamics of the vegetable market and provide data to support market participants in developing strategies to adapt to the changing environment.

Dog ecology and rabies knowledge, attitude and practice (KAP) in the Northern Communal Areas of Namibia.

In 2021, a comprehensive dog demographic questionnaire combined with a KAP survey were conducted in the northern communal areas (NCAs) of Namibia with the aim of gaining a better understanding of dog populations, owner behaviour, and knowledge, attitudes and practices (KAP) relating to rabies. The survey of 3,726 households across the eight regions of the NCAs provided insights that will inform interventions in order to improve human rabies prevention and Namibia's dog rabies control strategy. The results showed a relatively low average human/dog ratio (HDR) of 5.4:1 indicating a surprisingly high dog population of at least 272,000 dogs in the NCAs, 93% of which appear to be owned but are free-roaming. Data analysis revealed opportunities but also highlighted needs for improvements in rabies surveillance and mass dog vaccinations. Although knowledge, attitude, and practice scores towards epidemiologic and clinical aspects, human rabies prevention, and dog rabies vaccination were deemed to be acceptable, the survey nevertheless revealed deficiencies in certain aspects in some of the population. Interestingly, data seemed to indicate relatively high dog bite incidences per 100,000 people, ranging between 262 and 1,369 and a certain number of unreported human rabies cases. Despite the very high number of dogs, only 50% of dog-owning households reported having vaccinated their dogs. In order to address these issues, the planning, announcement, and implementation of mass dog vaccination campaigns needs to be adapted to achieve adequate vaccination coverage. Another focus needs to be on rabies awareness and education if Namibia is to be significantly contributing to the global goal of "Zero by 30".

Analysis of Sprite Activity in Middle Latitudes

Sprite activity in the middle latitudes of the Northern Hemisphere is analyzed, with the example of the territory of Russia, aiming to facilitate the planning of observational campaigns in the region. The global model previously proposed by the authors is applied, using data from the WWLLN lightning detection network for 2015–2021. It is shown that the total number of sprites per year varies widely, from 394 in 2019 to 2354 in 2015. The most intense sprite activity almost always occurs in July, but in some years, there may be a shift to June (2015) and August (2021). The highest frequency of sprite initiation is observed in the Krasnodar Territory, Altai, and the Far East. Altai shows a high intensity of sprite activity every year, which is caused by the relief and underlying surface, while Krasnodar Territory and the Far East demonstrate sprites developed by incoming convective systems, which leads to high year-to-year variability.

DEFENDING HUMAN RIGHTS IN THE ERA OF DATAFICATION

In this paper, we explore how activists and human rights defenders deal with datafication. This work demonstrates how data can be a valuable resource in activism and campaign planning. In addition, data and lack of data also complicate daily life for people in vulnerable positions, for example, when contacting government agencies, schools, and medical facilities . Data from four types of human rights activism formed the basis of our analysis. They include volunteers and employees of NGOs dealing with refugee and migrant issues, homelessness, poverty, sexual minorities, and women's shelters. The study was done in Sweden, where the GDPR (General Data Protection Regulation) laws limit the handling and storage of personal data. The following five major themes emerge from the analysis of data from our interview study: Affording personal integrity, Data poverty, Protective data practices, Drawing attention to data, and Systems and data routines. In addition, this study shows how activists and the organizations that they support are exposed to contradictory aspects of data; on one hand, deliberately exposing data about marginalized/minoritized groups, while on the other, making sure those groups, along with activists themselves, are not exposed. Most important, the data laws and regulations are not adjusted to the needs of the most vulnerable in society, and therefore, actions of civil disobedience are necessary to care for vulnerable populations through data.

ARTIFICIAL INTELLIGENCE IN POLITICAL CAMPAIGNS

Modern political campaigns are in constant flux and are influenced by numerous factors. Voters are constantly on the move, making segmentation significantly challenging. Simultaneously, literacy and education levels among the electorate are increasing every day, leading to a more critical attitude towards the persuasion process. In addition, changes in technology and the development of infor‑ mation and communication systems directly and drastically impact the shaping and management of modern campaigns. Artificial intelligence, machine learning, and deep learning have been influenc‑ ing the creation of modern political campaigns for a decade. The significance of artificial intelligence enhances the power of big data, algorithms, and, consequently, social networks. The authors explore the impact of artificial intelligence on the planning and execution of political campaigns and the resulting consequences for political candidates, parties, and society. Keywords: artificial intelligence (AI), political campaigns, big data, algorithms, voters, ethics