North Gujarat in India currently extracts three billion cubic meters of groundwater per year, which is up to 95% of the groundwater resources available in the region. This unsustainable abstraction has led to changes in groundwater levels and created water scarcity in many parts of the region. To address these issues, integrated groundwater resource management is required, which should be driven by good quality and quantity of groundwater data. However, current groundwater data are scarce; thus, new, affordable monitoring approaches are necessary. Participatory and community-based monitoring involving citizen scientists provides an approach to complement existing government-run monitoring. This study demonstrates the feasibility of developing a large-scale groundwater level monitoring wells network by directly involving farmers in two agriculturally-dominated blocks in North Gujarat, India. First, long-term groundwater level data for government-monitored wells were analyzed, and the regions lacking monitoring were identified. Then a network of 43 farmers was established through the field survey, who were trained to provide groundwater level observations for their wells every month. The data collected through the field survey were then integrated with the data from the existing government monitoring programs to understand the groundwater dynamics in the region. Results for the post-monsoon season 2022 show that the groundwater levels in Unjha block (Mehsana district) have declined to more than 100 meters below ground level due to unsustainable pumping for irrigation. The evaluation of the participatory approach showed that concern for existing groundwater challenges, social inclusion and contribution to scientific knowledge were the top three reasons that motivated farmers to participate in this research. Of the total volunteering farmers, 71% have shown interest in providing long-term observations for up to 3 years, and 57% agreed to provide observations weekly. Additionally, 70% of the farmers agreed to engage fellow farmers in groundwater monitoring, and 50% agreed to train new farmers. Thus, this study shows that farmers can play an important role in improving the existing challenges of groundwater monitoring through participatory training, and the integration of primary and secondary data can lead to better decision-making regarding need for well construction, crop selection, recharge methods and pathways for sustainable groundwater management.

Plant organ morphogenesis spans several orders of magnitude in time and space. Because of limitations in live-imaging, analysing whole organ growth from initiation to mature stages typically rely on static data sampled from different timepoints and individuals. We introduce a new model-based strategy for dating organs and for reconstructing morphogenetic trajectories over unlimited time windows based on static data. Using this approach, we show that Arabidopsis thaliana leaves are initiated at regular 1-day intervals. Despite contrasted adult morphologies, leaves of different ranks exhibited shared growth dynamics, with linear gradations of growth parameters according to leaf rank. At the sub-organ scale, successive serrations from same or different leaves also followed shared growth dynamics, suggesting that global and local leaf growth patterns are decoupled. Analysing mutants leaves with altered morphology highlighted the decorrelation between adult shapes and morphogenetic trajectories, thus stressing the benefits of our approach in identifying determinants and critical timepoints during organ morphogenesis.

Measles resurged in Vietnam between 2018 and 2020, especially in the Southern region. The proportion of children with measles infection showed quite some variation at the provincial level. We applied a spatio-temporal endemic–epidemic modelling framework for age-stratified infectious disease counts using measles surveillance data collected in Southern Vietnam between 1 January 2018 and 30 June 2020. We found that disease transmission within age groups was greatest in young children aged 0–4 years whereas a relatively high between-group transmission was observed in older age groups (5–14 years, 15–24 years and 25+ years groups). At the provincial level, spatial transmission followed an age-dependent distance decay with measles spread mainly depending on local and neighbouring transmission. Our study helped to clarify the measles transmission dynamics in a more detailed fashion with respect to age strata, time and space. Findings from this study may help determine proper strategies in measles outbreak control including promotion of age-targeted intervention programmes in specific areas.

Kansas City, Missouri, became one of the major United States hotspots for COVID-19 due to an increase in the rate of positive COVID-19 test results. Despite the large numbers of positive cases in Kansas City, MO, the spatial-temporal analysis of data has been less investigated. However, it is critical to detect emerging clusters of COVID-19 and enforce control and preventive policies within those clusters. We conducted a prospective Poisson spatial-temporal analysis of Kansas City, MO data to detect significant space-time clusters of COVID-19 positive cases at the zip code level in Kansas City, MO. The analysis focused on daily infected cases in four equal periods of 3 months. We detected temporal patterns of emerging and re-emerging space-time clusters between March 2020 and February 2021. Three statistically significant clusters emerged in the first period, mainly concentrated in downtown. It increased to seven clusters in the second period, spreading across a broader region in downtown and north of Kansas City. In the third period, nine clusters covered large areas of north and downtown Kansas City, MO. Ten clusters were present in the last period, further extending the infection along the State Line Road. The statistical results were communicated with local health officials and provided the necessary guidance for decision-making and allocating resources (e.g., vaccines and testing sites). As more data become available, statistical clustering can be used as a COVID-19 surveillance tool to measure the effects of vaccination.

Pertussis epidemics have displayed substantial spatial heterogeneity in countries with high socioeconomic conditions and high vaccine coverage. This study aims to investigate the relationship between pertussis risk and socio-environmental factors on the spatio-temporal variation underlying pertussis infection. We obtained daily case numbers of pertussis notifications from Queensland Health, Australia by postal area, for the period January 2006 to December 2012. A Bayesian spatio-temporal model was used to quantify the relationship between monthly pertussis incidence and socio-environmental factors. The socio-environmental factors included monthly mean minimum temperature (MIT), monthly mean vapour pressure (VAP), Queensland school calendar pattern (SCP), and socioeconomic index for area (SEIFA). An increase in pertussis incidence was observed from 2006 to 2010 and a slight decrease from 2011 to 2012. Spatial analyses showed pertussis incidence across Queensland postal area to be low and more spatially homogeneous during 2006–2008; incidence was higher and more spatially heterogeneous after 2009. The results also showed that the average decrease in monthly pertussis incidence was 3·1% [95% credible interval (CrI) 1·3–4·8] for each 1 °C increase in monthly MIT, while average increase in monthly pertussis incidences were 6·2% (95% CrI 0·4–12·4) and 2% (95% CrI 1–3) for SCP periods and for each 10-unit increase in SEIFA, respectively. This study demonstrated that pertussis transmission is significantly associated with MIT, SEIFA, and SCP. Mapping derived from this work highlights the potential for future investigation and areas for focusing future control strategies.