Forty-four years of land use changes in a Sardinian cork oak agro-silvopastoral system: a qualitative analysis

The island of Sardinia is the biggest producer of natural cork in Italy. In this study, cork oak cover change is investigated in a typical agro-silvopastoral system where the main activities are cereal fodder and wheat cultivation, sheep rearing and cork exploitation. The research method is based on the comparison of two land use maps produced by photo-interpretation of digitised aerial photographs taken in 1954 and 1998, combined with interviews with local farmers, field surveys, and data collected from literature, administrative documentation and decadal censuses (at council level). The results show that the cork oak woodland surface decreased (-29%). It was substituted by other forest, ploughed land, and mixed grassland and shrubland. Apart from the transformation of the cork oak woodland to other forest, other changes have happened probably because of an increase in agricultural and pastoral activities as described by the documental material available for the same area

A geostatistical analysis of the association between armed conflicts and P. falciparum malaria in Africa 1997-2010

Background The absence of conflict in a country has been cited as a crucial factor affecting the operational feasibility of achieving malaria control and elimination, yet mixed evidence exists on the influence that conflicts have had on malaria transmission. Over the past two decades, Africa has seen substantial numbers of armed conflicts of varying length and scale, creating conditions that can disrupt control efforts and impact malaria transmission. However, very few studies have quantitatively assessed the associations between conflicts and malaria transmission, particularly in a consistent way across multiple countries. Methods In this analysis an explicit geostatistical, autoregressive, mixed model is employed to quantitatively assess the association between conflicts and variations in Plasmodium falciparum parasite prevalence across a 13-year period in sub-Saharan Africa. Results Analyses of geolocated, malaria prevalence survey variations against armed conflict data in general showed a wide, but short-lived impact of conflict events geographically. The number of countries with decreased P. falciparum parasite prevalence (17) is larger than the number of countries with increased transmission (12), and notably, some of the countries with the highest transmission pre-conflict were still found with lower transmission post-conflict. For four countries, there were no significant changes in parasite prevalence. Finally, distance from conflicts, duration of conflicts, violence of conflict, and number of conflicts were significant components in the model explaining the changes in P. falciparum parasite rate. Conclusions The results suggest that the maintenance of intervention coverage and provision of healthcare in conflict situations to protect vulnerable populations can maintain gains in even the most difficult of circumstances, and that conflict does not represent a substantial barrier to elimination goals

Development and deployment of an improved Anopheles gambiae s.l. field surveillance by adaptive spatial sampling design

Introduction: Accurate assessments of vector occurrence and abundance, particularly in widespread vector-borne diseases such as malaria, are crucial for the efficient deployment of disease surveillance and control interventions. Although previous studies have explored the benefits of adaptive sampling for identifying disease hotspots (mostly through simulations), limited research has been conducted on field surveillance of malaria vectors. Methods: We developed and implemented an adaptive spatial sampling design in southwestern Benin, specifically targeting potential and uncertain Anopheles gambiae hotspots, a major malaria vector in sub-Saharan Africa. The first phase of our proposed design involved delineating ecological zones and employing a proportional lattice with close pairs sampling design to maximize spatial coverage, representativeness of ecological zones, and account for spatial dependence in mosquito counts. In the second phase, we employed a spatial adaptive sampling design focusing on high-risk areas with the greatest uncertainty. Results: The adaptive spatial sampling design resulted in a reduced sample size from the first phase, leading to improved predictions for both out-of-sample and training data. Collections of Anopheles gambiae in high-risk and low-uncertainty areas were nearly tripled compared to those in high-risk and high-uncertainty areas. However, the overall model uncertainty increased. Discussion: While the adaptive sampling design allowed for increased collections of Anopheles gambiae mosquitoes with a reduced sample size, it also led to a general increase in uncertainty, highlighting the potential trade-offs in multi-criteria adaptive sampling designs. It is imperative that future research focuses on understanding these trade-offs to expedite effective malaria control and elimination efforts

Joint spatial modelling of malaria incidence and vector's abundance shows heterogeneity in malaria‐vector geographical relationships

Limited attention from the modelling community has been given to ecological approaches which aim to predict geographical patterns of malaria by accounting for the joint effects of different vectors and environmental drivers. A hierarchical multivariate joint spatial Gaussian generalised linear model was developed to provide joint parameters inference and mapping of counts of Anopheles gambiae, An. funestus, An. nili and malaria incidence collected in an area of Cote d'Ivoire. Variable‐selection methods were applied to select important predictors for each mosquito species and malaria incidence. The proposed joint model led to a general reduction of the variance in the estimates compared to independent modelling. There was high variability in the composition of Anopheles mosquito species in the villages with each species suitability only partly overlapping geographically. Abundances of An. gambiae, An. funestus and An. nili were primarily determined by temperature. None of the species were found as a significant predictor for the others. Anopheles gambiae was the predominant species and only An. gambiae female abundance was an important variable (linear predictor) for malaria incidence. However, the geographic correlation analyses show that the rest of Anopheles species are likely playing a role in malaria suitability. Residuals from the models of mosquito abundance and malaria cases are also correlated with each other and overlapping but in geographic patches, meaning that local drivers of vector‐malaria suitability are still present and not represented by the predictors used in the model. Synthesis and applications: Joint modelling improve predictive estimation compared to individual modelling. The accurate predictions highlighted high diversity in the association between malaria and vector species, with most of the area having more than one species suitability correlated with malaria suitability. These zones are unlikely to benefit from species‐specific interventions. Areas with correlated malaria and vector species suitability residuals contain local information, not included in the model, that requires further investigation. This will identify additional communal malaria and vectors factors that need to be considered for optimal malaria control and elimination strategies since these factors are expected to be linked to the local malaria transmission

Education and Socio‑economic status are key factors influencing use of insecticides and malaria knowledge in rural farmers in Southern Côte d’Ivoire

Background Insecticides play a key role in rural farming; however, their over- or misuse has been linked with a negative impact on malaria vector control policies. This study was conducted amongst agricultural communities in Southern Côte d’Ivoire to identify which insecticides are used by local farmers and how it relates to the perception of farmers on malaria. Understanding the use of insecticides may help in designing awareness programme on mosquito control and pesticides management. Methods A questionnaire was administered to 1399 farming households across ten villages. Farmers were interviewed on their education, farming practices (e.g. crops cultivated, insecticides use), perception of malaria, and the different domestic strategies of mosquito control they use. Based on some pre-defined household assets, the socioeconomic status (SES) of each household was estimated. Statistical associations were calculated between different variables, showing significant risk factors. Results The educational level of farmers was significantly associated with their SES (p < 0.0001). Most of the householders (88.82%) identified mosquitoes as the principal cause of malaria, with good knowledge of malaria resulting as positively related to high educational level (OR = 2.04; 95%CI: 1.35, 3.10). The use of indoor chemical compounds was strongly associated to the SES of the households, their education level, their use of ITNs and insecticide in agricultural (p < 0.0001). Indoor application of pyrethroid insecticides was found to be widespread among farmers as well as the use of such insecticide for crops protection. Conclusion Our study shows that the education level remains the key factor influencing the use of insecticides by farmers and their awareness of malaria control. We suggest that better communication tailored to education level and including SES, controlled availability and access to chemical products, should be considered when designing campaigns on use of pesticides and vector borne disease control for local communities

A review of the main genetic factors influencing the course of COVID-19 in Sardinia: the role of human leukocyte antigen-G

Introduction: A large number of risk and protective factors have been identified during the SARS-CoV-2 pandemic which may influence the outcome of COVID-19. Among these, recent studies have explored the role of HLA-G molecules and their immunomodulatory effects in COVID-19, but there are very few reports exploring the genetic basis of these manifestations. The present study aims to investigate how host genetic factors, including HLA-G gene polymorphisms and sHLA-G, can affect SARS-CoV-2 infection. Materials and Methods: We compared the immune-genetic and phenotypic characteristics between COVID-19 patients (n = 381) with varying degrees of severity of the disease and 420 healthy controls from Sardinia (Italy). Results: HLA-G locus analysis showed that the extended haplotype HLA-G*01:01:01:01/UTR-1 was more prevalent in both COVID-19 patients and controls. In particular, this extended haplotype was more common among patients with mild symptoms than those with severe symptoms [22.7% vs 15.7%, OR = 0.634 (95% CI 0.440 – 0.913); P = 0.016]. Furthermore, the most significant HLA-G 3’UTR polymorphism (rs371194629) shows that the HLA-G 3’UTR Del/Del genotype frequency decreases gradually from 27.6% in paucisymptomatic patients to 15.9% in patients with severe symptoms (X2 = 7.095, P = 0.029), reaching the lowest frequency (7.0%) in ICU patients (X2 = 11.257, P = 0.004). However, no significant differences were observed for the soluble HLA-G levels in patients and controls. Finally, we showed that SARS-CoV-2 infection in the Sardinian population is also influenced by other genetic factors such as β-thalassemia trait (rs11549407C&gt;T in the HBB gene), KIR2DS2/HLA-C C1+ group combination and the HLA-B*58:01, C*07:01, DRB1*03:01 haplotype which exert a protective effect [P = 0.005, P = 0.001 and P = 0.026 respectively]. Conversely, the Neanderthal LZTFL1 gene variant (rs35044562A&gt;G) shows a detrimental consequence on the disease course [P = 0.001]. However, by using a logistic regression model, HLA-G 3’UTR Del/Del genotype was independent from the other significant variables [ORM = 0.4 (95% CI 0.2 – 0.7), PM = 6.5 x 10-4]. Conclusion: Our results reveal novel genetic variants which could potentially serve as biomarkers for disease prognosis and treatment, highlighting the importance of considering genetic factors in the management of COVID-19 patients

The spatial and temporal scales of local dengue virus transmission in natural settings:a retrospective analysis

Background Dengue is a vector-borne disease caused by the dengue virus (DENV). Despite the crucial role of Aedes mosquitoes in DENV transmission, pure vector indices poorly correlate with human infections. Therefore there is great need for a better understanding of the spatial and temporal scales of DENV transmission between mosquitoes and humans. Here, we have systematically monitored the circulation of DENV in individual Aedes spp. mosquitoes and human patients from Caratinga, a dengue endemic city in the state of Minas Gerais, in Southeast Brazil. From these data, we have developed a novel stochastic point process pattern algorithm to identify the spatial and temporal association between DENV infected mosquitoes and human patients. Methods The algorithm comprises of: (i) parameterization of the variogram for the incidence of each DENV serotype in mosquitoes; (ii) identification of the spatial and temporal ranges and variances of DENV incidence in mosquitoes in the proximity of humans infected with dengue; and (iii) analysis of the association between a set of environmental variables and DENV incidence in mosquitoes in the proximity of humans infected with dengue using a spatio-temporal additive, geostatistical linear model. Results DENV serotypes 1 and 3 were the most common virus serotypes detected in both mosquitoes and humans. Using the data on each virus serotype separately, our spatio-temporal analyses indicated that infected humans were located in areas with the highest DENV incidence in mosquitoes, when incidence is calculated within 2.5–3 km and 50 days (credible interval 30–70 days) before onset of symptoms in humans. These measurements are in agreement with expected distances covered by mosquitoes and humans and the time for virus incubation. Finally, DENV incidence in mosquitoes found in the vicinity of infected humans correlated well with the low wind speed, higher air temperature and northerly winds that were more likely to favor vector survival and dispersal in Caratinga. Conclusions We have proposed a new way of modeling bivariate point pattern on the transmission of arthropod-borne pathogens between vector and host when the location of infection in the latter is known. This strategy avoids some of the strong and unrealistic assumptions made by other point-process models. Regarding virus transmission in Caratinga, our model showed a strong and significant association between high DENV incidence in mosquitoes and the onset of symptoms in humans at specific spatial and temporal windows. Together, our results indicate that vector surveillance must be a priority for dengue control. Nevertheless, localized vector control at distances lower than 2.5 km around premises with infected vectors in densely populated areas are not likely to be effective

Analisi variografica del diametro di un impianto di quercia da sughero. Un esempio di studio della corregionalizzazione in ambito forestale

Geostatistics provides tools to model, estimate, map and predict spatial patterns of tree size and growth. Variogram models were used to study spatial dependence of basal diameter in young cork oak plantation. Our objective was to determine the spatial dependence of stand by studying the related spatial stochastic model. Despite the fact that high nugget effects affect the model for whole area, cork oak diameter shows a better spatial dependence in the two different geological classes that divide the area. An isotropic exponential and an isotropic spherical variogram were the models chosen to represent granite and quartzite subareas. In both models, cork oak diameter was spatially autocorrelated over distances no greater than 8 m, a measure of average patch diameter in this forest ecosystem. The results of survey suggest that geology exerts a strong control on cork oak diameter variation, and in the whole area that other factors are also involved, such as elevation, soil depth, catchment area, slope, hillshade and aspect are creating a general random effect. Simulation models and area estimates of tree performance in young or old-growth forests may be improved by including geostatistical components to summarize ecological spatial dependence

Food insecurity, mental health and in-hospital mortality following the COVID-19 pandemic in a socially deprived UK coastal town

Background Public health interventions are essential to prevent a long tail of costly, avoidable and worsening ill health in coastal communities following the COVID-19 pandemic, yet no research exists to guide policy and practice as to which groups within coastal communities are vulnerable and most in need of such interventions. Within this aim, we explore engrained and emerging vulnerabilities of food insecurity, health and well-being for different demographic groups within the deprived coastal community of Fleetwood, Lancashire, UK, before and after the pandemic.Methods Routinely collected data of free school meal eligibility, community mental health referrals and hospital admissions between 28 March 2016 and 31 December 2021 were aggregated by locality and deprivation within Fleetwood. Temporal autoregressive models, generalised linear mixed models and survival analyses were employed to compare trends and associations in food insecurity, health and well-being indicators against deprivation indices, demographics, comorbidities (including COVID-19), the COVID-19 pandemic period and locality.Results Areas with better housing and income, but higher health and disability deprivation, showed increased levels of free school meal eligibility following the pandemic. Mental health was insensitive to the first 14 months of pandemic yet is worsened by unemployment deprivation and cardiovascular and respiratory comorbidities, with a greater predisposition to poor mental health in adolescents and young adults. After accounting for the effect of COVID-19, hospital mortality risk increased with demographic influences in fitting with the typology of coastal communities having an older population, struggling healthcare and a greater prevalence of comorbidities.Conclusions Public health managers and policy makers seeking to prevent worsening health and well-being within coastal communities following the pandemic should focus on broader-scale patterns reflecting entrenched poor health typical of coastal communities, and emerging food insecurity within specific demographic and deprivation groups at finer scales.Data are available on reasonable request