Peak Date Research Articles

Pollen season variations among anemophilous species in an Atlantic-influenced mediterranean environment: a long term study (1993-2022).

Numerous studies have shown that climate change is impacting the airborne pollen concentration of various anemophilous plant species, both herbaceous and woody. The aim of this study was to determine whether this trend, highly marked in Europe, is also occurring in the city of Huelva (south-western Spain), an area with a milder Mediterranean climate. The factors influencing the main pollen season were studied for all local taxa accounting for more than 1% of the total pollen collected, over the period 1993-2022, using a volumetric Hirst type trap to determine concentration. The study included a trend analysis of meteorological variables susceptible to climate change, specifically maximum, minimum and average temperature, precipitation and the number of hours of insolation per day. Although some of the variables analysed do not show significant trends for most taxa, such as start date or peak date, the overall results indicate a trend in the airborne pollen dynamics for herbaceous and woody taxa. Six of the eight woody taxa studied showed a significant trend towards increased pollen concentration, some of which are associated with temperature in previous seasons. In contrast, three of the five herbaceous taxa, which are more sensitive to short term meteorological variables, show a significant acceleration in the end date of the main pollen season, correlated negatively with temperature and insolation. In conclusion, some incipient changes are occurring, which may be indicative of future consequences on biodiversity in the Mediterranean area influenced by the Atlantic Ocean.

Flowering seasonality and airborne pollen recent trends in Sierra de las Nieves, the southernmost National Park in continental Spain

Sierra de las Nieves is the southernmost National Park in continental Spain and, in a global warming scenario, it is important to determine the impacts of climatic variations on the vegetation, with special relevance to their reproductive cycles. The flowering seasonality and intensity of the dominant anemophilous species usually reflect the response of the vegetation to climate variations, which can be monitored through the atmospheric pollen concentrations. In this study, airborne pollen was analysed for 6 years (2018–2023). A pollen calendar was elaborated to show the flowering seasonality and intensity of the dominant anemophilous taxa. Spring was the season with the highest airborne pollen diversity and intensity, Quercus being the pollen type with the highest concentrations. We observed that weather conditions influenced the presence of airborne pollen. Precipitation and relative humidity reduce pollen concentrations, while temperature rises increase them during the pre-peak period and reduce them during the post-peak. Wind dynamics play different roles depending on the pollen type considered given the heterogeneous distribution of the pollen emission sources. Significant advances were detected in the start date of the main pollen season of Quercus and Olea, as well as in the peak date of Quercus. On the contrary, other taxa such as Pinus or Poaceae did not show any trend. This brings to the fore that the responses to climatic variations may differ from one taxon to another, a continuous study in natural areas being required for detecting possible changes in the observed trends.

Impact of Climate Change on Peach Fruit Moth Phenology: A Regional Perspective from China.

It is widely recognized that the phenology of insects, of which the life activities are closely tied to temperature, is shifting in response to global climate warming. This study aimed to investigate the impacts of climate change on the phenology of Carposina sasakii Matsumura, 1900 (Lepidoptera: Carposinidae) across large temporal and spatial scales, through collecting and systematically analyzing historical data on the pest's occurrence and population dynamics in China. The results showed that for overwintering adults, the first occurrence date in eastern, northwestern, and northern China has significantly advanced, along with the population peak in eastern and northwestern China. At the provincial level, the population peak date in Shandong province has also moved significantly earlier, as well as the population peak date in Shandong and Shaanxi and the end occurrence date in Ningxia. However, the population peak date in Jilin has experienced a delayed trend. For first-generation adults, the first occurrence date in northeastern, eastern, and central China has notably advanced, while the first appearance date in northwestern and northern China has significantly delayed. Additionally, the population peak in northwestern China has experienced significant delays, along with the final occurrence in northeastern and northwestern China. At the provincial level, the first occurrence date in Liaoning, Shandong, and Shanxi has significantly advanced, while Hebei has demonstrated a significant delay. The population peak time in Gansu and Shaanxi has displayed significant delays, and the end occurrence date in Liaoning, Shanxi, and Shaanxi has also shown significant delays. Furthermore, these findings integrated with the Pearson correlation results reveal spatial heterogeneity in C. sasakii's phenological responses to climate warming at both regional and provincial scales. The phenology of C. sasakii and their changing patterns with climate warming vary by geographical location. This study provides valuable information for the future monitoring, prediction, and prevention of peach fruit moths in the context of climate warming.

Epidemiological characteristics of human rabies cases reported by sites in China from 2006 to 2022

BackgroundRabies is an incessant public health threat in China. The Ministry of Health implemented the Central Payment for Rabies Prevention and Control Project to assist with rabies prevention and control in a few representative provinces in 2006.MethodsData on human rabies cases reported by the National Infectious Disease Reporting Information Management System and national surveillance sites from 2006 to 2022 were collected, and statistical and multivariate analyses were then used to assess the effectiveness of current prevention and control efforts.ResultsDuring 2006–2022, a total of 2025 human rabies cases were collected by the national surveillance sites, with incidence rates far above the national average, but the incidence rate was consistent with the national trend. Human rabies cases demonstrated a dual peak distribution in terms of exposure and onset dates, with the peak exposure dates falling mostly in the spring and summer and the peak onset dates occurring mostly in the summer and autumn. Three danger categories are shown by the geographical distribution: high, medium and low. Dogs had a high infection rate (86.93%), with own domesticated dogs accounting for the majority of infections. The rates of post-exposure prophylaxis are not constant. The median incubation period was 71 days.ConclusionsVarious measures and policies implemented by the government have played a key role in reducing the incidence of rabies. To effectively prevent and control the resurgence of epidemics and halt the spread of the virus among host animals, it is imperative to prioritize and implement a robust dog management system, accelerate research and development of animal vaccines and improve the level of post-exposure prophylaxis.

Mapping the Characteristics of Respiratory Infectious Disease Epidemics in China Based on the Baidu Index from November 2022 to January 2023.

Infectious diseases pose a significant global health and economic burden, underscoring the critical need for precise predictive models. The Baidu index provides enhanced real-time surveillance capabilities that augment traditional systems. Baidu search engine data on the keyword "fever" were extracted from 255 cities in China from November 2022 to January 2023. Onset and peak dates for influenza epidemics were identified by testing various criteria that combined thresholds and consecutive days. The most effective scenario for indicating epidemic commencement involved a 90th percentile threshold exceeded for seven consecutive days, minimizing false starts. Peak detection was optimized using a 7-day moving average, balancing stability and precision. The use of internet search data, such as the Baidu index, significantly improves the timeliness and accuracy of disease surveillance models. This innovative approach supports faster public health interventions and demonstrates its potential for enhancing epidemic monitoring and response efforts.

Comparison of Olea pollen sampling between gravimetric and volumetric traps (NW of Morocco)

Olea pollen is considered one of the most critical causes of respiratory allergic disease in the Mediterranean region, contributing to different symptoms in allergic sufferers. Due to the high increase in the prevalence of olive pollen caused by the expansion of cultivated areas especially in the Mediterranean region, the aim of this study was to examine the Olea pollen sampling efficiency between gravimetric and volumetric pollen traps over the period 2018–2022. Our data demonstrated that Olea pollen season start-date was generally recorded earlier with volumetric trap than shown with gravimetric method. Moreover, the peak dates occurred mainly in May for the years studied. Across the entire period, the greatest pollen concentrations were reached in the week 11th and >60 % of the total Olea pollen were already reached after the ninth week of sampling in 2020. In general, the evolution of pollen sampling for both methods demonstrates similar temporal development. Variations in the seasonal pollen integral could be attributed to the elevation above ground level at which the traps were placed, exposure to regional winds, the influence of long-range transported pollen and the mode of pollen transport. The findings indicate that the gravimetric method was equally practical as the volumetric method in estimating airborne pollen.

Forecast models for start and peak dates of Poaceae pollen season in Tétouan (NW Morocco) using multiple regression analysis.

Poaceae pollen is one of the most widespread sources of aeroallergens in the world. The aim of this study is to build predictive models for the pollen season start day (PSsd) and peak dates of the Poaceae pollen season and thus give an overview of the climatic parameters that have the greatest influence. In Tétouan, sampling was carried out using a volumetric spore trap of the Burkard Hirst type. The relationships between the PSsd, peak dates and meteorological parameters were determined using correlation analysis. The models were constructed using multiple regression analysis with data from 2008 to 2019 and tested on data from 2022. The PSsd was especially significantly influenced by minimum temperature during winter and precipitation in the autumn of the previous year. The peak dates were significantly correlated with precipitation in January, March and April, but not with temperature. Three models were obtained for each of the season's parameters; the most accurate model for the PSsd explained a variability of 61% and includes as main predictors rainfall from the autumn of the previous year and the mean daily average temperature from 23 February to 8 March. The two most efficient peak dates models included precipitation in January and April as the main predictor variables, and explained greater variability (87 and 88%). Precipitation in autumn and the mean daily and the sum of minimum temperature in winter, showed significant decreasing tendencies. However, the PSsd trend delay was not statistically significant. This study draws the importance of the weather during preseason for grass pollen production and emphasises the usefulness of the models for allergic patients to take preventive measures and for healthcare professionals in allergy therapy.

Regional differences in the impact paths of climate on aboveground biomass in alpine grasslands across the Qinghai-Tibet Plateau

Alpine grasslands on the Qinghai-Tibet Plateau (QTP) play an essential role in water conservation, biodiversity protection and climate feedback, with aboveground biomass (AGB) serving as a crucial indicator of grassland health and functionality. While previous studies have independently explored the phenological differences, cumulative effects, and spatial variability of climatic impacts on biomass/productivity in alpine grasslands, the cascading effects regarding climate and phenology on AGB still present knowledge gaps. Here, using peak AGB measurements, remote sensing and gridded climate data in the QTP alpine grasslands during 2002–2018, we systematically analyzed the impact paths of climatic variables (i.e., cumulative precipitation, CP; growing degree-days, GDD) and phenology-mediated paths (start and peak date of the growing season, SOS and POS) on AGB and their regional differences. During the preseason (pre60) or the growing season (sos-pos), climate primarily directly impacted variations in AGB across different climatic regions, although a phenology-mediated path by which climate indirectly affected AGB existed (i.e., GDDsos-pos → POS → AGB). Three general patterns were revealed: In the plateau temperate arid regions, an increase in CPpre60 significantly promoted AGB (path coefficients w = 0.61–0.71), whereas an increase in GDDpre60 inhibited AGB (w = −0.42 ~ −0.49); In the plateau sub-cold regions, increases in both CPsos-pos and GDDsos-pos significantly promoted AGB, respectively (w = 0.46–0.81 and w = 0.37–0.70); Similarly, in the plateau temperate arid or semi-arid regions, increases in CPsos-pos also significantly promoted the AGB (w = 0.56–0.73). This study highlights that the water and heat accumulation mainly exert direct impacts on alpine grassland AGB across various climatic regions and phenological stages, providing insights into the mechanism driving AGB by climate and phenology during spring and summer.

How Climate Warming Influences the Phenology of Grapholita molesta (Busck, 1916) (Lepidoptera: Tortricidae) in China: Insight from Long-Term Historical Data.

Grapholita molesta (Busck, 1916), a significant pest affecting various fruits such as pears, apples, peaches, etc., is highly adaptable to changing temperatures. However, the phenological response mechanism of this pest to climate warming remains unclear. To address this issue, we collected population dynamics data of G. molesta in China over the years along with corresponding climate data. We analyzed five phenological indexes: the first, end, and peak occurrence dates of contemporary adults as well as the first and peak occurrence dates of overwintering adults in China. Results revealed an upward trend in the annual average temperature and average temperature of the four seasons in regions infested by G. molesta in eastern, northeastern, northwestern, northern, and southwestern China from 1980 to 2020. Notably, the population peak date of overwintering adults in northeastern and eastern China significantly advanced along with the first occurrence date and the population peak date of overwintering adults in northern China. Additionally, the population peak date of contemporary adults in northwestern China significantly advanced. However, the end occurrence date of contemporary adults in northern China was significantly delayed, as was the first occurrence date of overwintering adults in northwestern China. Furthermore, our study demonstrated spatial heterogeneity in the phenological response of G. molesta to climate warming across China. This study elucidates the phenological response of G. molesta to climate warming, offering valuable insights for predicting future pest infestations and informing adaptive pest management strategies in fruit tree cultivation.

Прогнозирование характеристик половодья 2024 года на реках Ишим, Тобол и Урал

The Hydrometeorological Center of Russia has developed a system of methods for forecasting spring flood characteristics, which was implemented for eight stations on the Ishim, Tobol, and Ural rivers in 2024. Specific features of the methods are the simplicity of their obtaining and implementation for a particular river reach and a possibility of the fast correction of forecasts as the current information is available. The peak water level at the station was predicted based on its dependence on the peak water level at the upstream station. The date of the flood peak was predicted with account of the data of the peak at the upstream station and probable values of the travel time from the upstream gauge to the predicted downstream one. The date of the water level drop to the level of a severe event was forecasted taking into account the height and date of the flood peak using the calculated recession curve obtained by the statistical analysis of flood recessions in the previous years. The date of the water level drop to the level of an adverse event was predicted by extrapolating the flood recession observed during 10–15 days after the flood peak. The mean forecast lead time is 9 days. The forecasts were issued in a deterministic and probabilistic form and gave quite satisfactory results, which were used in the organization and implementation of measures to protect the population and economic facilities from the floods observed in 2024. The efficiency of the developed methods allows recommending them for use in predicting flood characteristics in various regions of Russia.

Analysis of a Plant’s Response to Climate Change Factors Through the Use of Herbarium Records: Collinsia violacea Nutt. (Plantaginaceae).

Climate change has resulted in various changes to the phenology of species, and some of these changes have been documented through the use of herbarium specimens. Understanding how plants react to changes in the environment can give scientists insight into how plants have been responding and will respond to the continuing consequences of climate change as well as how to approach biodiversity conservation. In this study, herbarium records of Collinsia violacea Nutt. ranging from 1895 to 2014 were utilized to show the trends of the first and peak flowering dates with regard to various geographic and climatic variables using regression analysis. The results from simple linear regression analyses showed a trend of the flowering times for first and peak flowering dates occurring earlier over the years; however, the relationship was not significant. The multiple linear regression full model for first flowering indicated increases in latitude, longitude, and mean monthly temperatures were associated with delayed flowering while increases in monthly minimum and maximum temperatures were associated with earlier flowering. The full model for peak flowering showed that peak flowering was delayed with increases in latitude, longitude, and maximum monthly temperature. The reduced models, with highly correlated variables removed, indicated significant delays in first flowering and peak flowering with increases in latitude, longitude, and mean monthly temperature, but no significant relationship between monthly precipitation and flowering time. Further research is needed to fully understand the implications of these changes.

A circumpolar study unveils a positive non‐linear effect of temperature on arctic arthropod availability that may reduce the risk of warming‐induced trophic mismatch for breeding shorebirds

AbstractSeasonally abundant arthropods are a crucial food source for many migratory birds that breed in the Arctic. In cold environments, the growth and emergence of arthropods are particularly tied to temperature. Thus, the phenology of arthropods is anticipated to undergo a rapid change in response to a warming climate, potentially leading to a trophic mismatch between migratory insectivorous birds and their prey. Using data from 19 sites spanning a wide temperature gradient from the Subarctic to the High Arctic, we investigated the effects of temperature on the phenology and biomass of arthropods available to shorebirds during their short breeding season at high latitudes. We hypothesized that prolonged exposure to warmer summer temperatures would generate earlier peaks in arthropod biomass, as well as higher peak and seasonal biomass. Across the temperature gradient encompassed by our study sites (>10°C in average summer temperatures), we found a 3‐day shift in average peak date for every increment of 80 cumulative thawing degree‐days. Interestingly, we found a linear relationship between temperature and arthropod biomass only below temperature thresholds. Higher temperatures were associated with higher peak and seasonal biomass below 106 and 177 cumulative thawing degree‐days, respectively, between June 5 and July 15. Beyond these thresholds, no relationship was observed between temperature and arthropod biomass. Our results suggest that prolonged exposure to elevated temperatures can positively influence prey availability for some arctic birds. This positive effect could, in part, stem from changes in arthropod assemblages and may reduce the risk of trophic mismatch.

Interactions between runoff volume, timing, and annual temperatures shape migration phenology of a threatened adfluvial sucker

AbstractMigratory fish species have been particularly impacted by changes to the hydrologic and climatic cues to which migration and spawning behaviours have been adapted across generations. While conservation and recovery programs increasingly implement flow management actions to promote successful migration and spawning, uncertainty regarding how spawning migration phenology responds to changing environmental conditions can limit the ability to effectively target such recovery actions. Here, we use a Bayesian hierarchical modelling framework to analyse spawning migration phenology of individually tagged June suckers (Chasmistes liorus) – a federally threatened, long‐lived, iteroparous, adfluvial species endemic to Utah Lake (Utah, USA) and its tributaries. We then examine how annual hydrologic and thermal conditions relate to different components of annual migration phenology, including peak migration date, in‐stream residence time, and among‐individual variation in migration timing. Peak migration date occurred earlier in years with warmer spring air temperatures (a proxy for water temperatures), though this effect interacted with peak runoff timing. Both residence time and among‐individual variation in migration timing were greater in years with larger spring discharge and later peak flows. Residence time was also longer in warmer years. These results highlight how natural and anthropogenic changes to river flow and thermal regimes are likely to impact June sucker migration timing and duration, and our approach can be applied to other migratory species to identify the external drivers of the different components of migration phenology.

Long-term aquarium records delineate the synchronized spawning strategy of Acropora corals.

Aligning spawning timing with seasonal environmental changes is critical for both terrestrial and aquatic organisms. However, mechanisms to regulate reproductive activity in response to environmental factors are not well understood, partly owing to the technical difficulty in maintaining detailed long-term observational data of the reproductive activities in the same population across years. In this study, we present an application of the aquarium system to examine the long-term spawning properties of corals. Spawning records over a 15-year period at the Okinawa Churaumi Aquarium revealed that the spawning timing of Acropora corals in aquarium tanks aligned well with that of wild corals from a neighbouring reef. Using the aquarium population as a model, we investigated the relationship between key environmental factors and the timing of the first and peak spawning dates of Acropora corals during a 15-year period. The results suggest that the spawning window of each spawning season is largely influenced by the water temperature and that the timing of peak spawning can be fine-tuned in response to environmental fluctuations. This behavioural feature can prevent synchronous spawning events during unfavourable environmental conditions and increase long-term reproductive reliability.

Changing phytoplankton phenology in the marginal ice zone west of the Antarctic Peninsula

Climate change is altering global ocean phenology, the timing of annually occurring biological events. We examined the changing phenology of the phytoplankton accumulation season west of the Antarctic Peninsula to show that blooms are shifting later in the season over time in ice-associated waters. The timing of the start date and peak date of the phytoplankton accumulation season occurred later over time from 1997 to 2022 in the marginal ice zone and over the continental shelf. A divergence was seen between offshore waters and ice-associated waters, with offshore bloom timing becoming earlier, yet marginal ice zone and continental shelf bloom timing shifting later. Higher chlorophyll a (chl a) concentration in the fall season was seen in recent years, especially over the northern continental shelf. Minimal long-term trends in annual chl a occurred, likely due to the combination of later start dates in spring and higher chl a in fall. Increasing spring wind speed is the most likely mechanism for later spring start dates, leading to deeper wind mixing in a region experiencing sea ice loss. Later phytoplankton bloom timing over the marginal ice zone and continental shelf will have consequences for surface ocean carbon uptake, food web dynamics, and trophic cascades.

Assessing Environmental Effects upon Modeling the Individual Lactation Curve of a Local Goat Population in a Pastoral System.

The present study aims to use Wood's model to determine the parameters of individual lactation curves in a local goat population and their factors of variation under a pastoral system. A total of 137,927 records from 432 local goats were collected to assess the impact of litter size, year and month of kidding, herd and the age of the dam on lactation curve parameters. Wood's model parameters were estimated using non-linear regression, and individual curves were fitted. The characteristics of the lactation curves were computed. The initial yield (A), rate of increase (B) and rate of decline (C) parameters in Wood's model for local goats were 730 g, 0.26 and 0.09 respectively. The values of peak milk production (PP), peak date (PD) and persistency (PC) were 931.88 g/d, 23.39 days and 91.50%, respectively. Persistence was higher in goats with simple births, while peak production increased by around 0.3 kg for each additional kid. The curve parameters "A" and "C" differed according to the herd and month of kidding (p < 0.05); the age of the dam only had an impact on parameter "A" (p < 0.01). Parameter "B" was not significantly influenced by any of the factors considered (p > 0.05). Correlation coefficients among lactation curve characteristics were ranged from -0.20 to 0.89. Due to a significant negative correlation, selecting for parameter "A" may have an adverse effect on parameter "B", resulting in a shorter time to reach peak production and less persistency, but an increase in peak production among goats. The curves derived from Wood's model suggest that the shape of the curve may serve as a basis for herd management planning and to improve local population potentialities.

Human movement avoidance decisions during Coronavirus disease 2019 in Japan

Understanding host behavioral change in response to epidemics is important to forecast the disease dynamics. To predict the behavioral change relevant to the epidemic situation (e.g., the number of reported cases), we need to know the epidemic situation at the moment of decision, which is difficult to identify from the records of actually performed human mobility. In this study, the largest travel accommodation reservation data covering half of the existed accommodations in Japan was analyzed to observe decision-making timings and how it responded to the changing epidemic situation during Japan’s Coronavirus Disease 2019 until February 2023. To this end, we measured mobility avoidance index proposed in Ito et al., 2022 to indicate people's decision of mobility avoidance and quantified it using the time-series of the accommodation booking/cancellation data. We observed matches of the peak dates of the mobility avoidance and the number of reported cases, and mobility avoidance changed proportional to the logarithmic number of reported cases. We also found that the slope of mobility avoidance against the change of the logarithmic number of reported cases were similar among the epidemic waves, while the intercept of that was much reduced as the first epidemic wave passed by. People measure the intensity of epidemic by logarithm of the number of reported cases. The sensitivity of their response is established during the first wave and the people's response became weakened after the first experience, as if the number of reported cases were multiplied by a constant small factor.

Beyond Fixed Dates and Coarse Resolution: Developing a Dynamic Dry Season Crop Calendar for Paddy in Indonesia from 2001 to 2021

There is valuable information that can be obtained beyond using a fixed crop calendar with coarse spatial resolution. Knowing the dynamics of the timing and location in which a particular crop is planted and harvested, with an annual temporal resolution and a fine spatial resolution, is crucial not only for monitoring crop conditions and production but also for understanding crop management under changing climates. In this study, the Normalized Difference Vegetation Index (NDVI) was utilized to develop a historical crop calendar for paddy in Indonesia with a 1 km resolution from 2001 to 2021. The result of this study is the first dynamic crop calendar that includes information about the planting, peak, and harvesting dates, as crop growth indicators, derived from the analysis of NDVI value fluctuations. Additionally, this dataset also includes the total number of cropping seasons each year. In Indonesia, there are intensive agricultural activities, including two dry cropping seasons that occur after the wet cropping season. However, this dataset is limited only to crops grown during the dry seasons, which typically begin in February and June. This dataset offers significant information at a finer spatiotemporal resolution to enable studies on agricultural fields undergoing climate change, although it is more country–specific than the other established dataset. The annual crop calendar dataset from 2001 to 2021 underscores the significance of examining the variability in cropping seasons over the years. This exploration aims to deepen our comprehension of the interplay between cropping seasons, climatic indicators, and even the social factors influencing farmers’ decisions. Furthermore, presented at a 1 km resolution, this dynamic crop calendar underscores the need for a more precise representation of diverse cropping intensities and seasons, particularly within small and fragmented agricultural areas.

Predicting the main pollen season of Broussonetia Papyrifera (paper mulberry) tree.

Paper mulberry pollen, declared a pest in several countries including Pakistan, can trigger severe allergies and cause asthma attacks. We aimed to develop an algorithm that could accurately predict high pollen days to underpin an alert system that would allow patients to take timely precautionary measures. We developed and validated two prediction models that take historical pollen and weather data as their input to predict the start date and peak date of the pollen season in Islamabad, the capital city of Pakistan. The first model is based on linear regression and the second one is based on phenological modelling. We tested our models on an original and comprehensive dataset from Islamabad. The mean absolute errors (MAEs) for the start day are 2.3 and 3.7 days for the linear and phenological models, respectively, while for the peak day, the MAEs are 3.3 and 4.0 days, respectively. These encouraging results could be used in a website or app to notify patients and healthcare providers to start preparing for the paper mulberry pollen season. Timely action could reduce the burden of symptoms, mitigate the risk of acute attacks and potentially prevent deaths due to acute pollen-induced allergy.

Basic Reproduction Number (R0), Doubling Time, and Daily Growth Rate of the COVID-19 Epidemic: An Echological Study.

In infectious diseases, there are essential indices used to describe the disease state. In this study, we estimated the basic reproduction number, R0, peak level, doubling time, and daily growth rate of COVID-19. This ecological study was conducted in 5 provinces of Iran. The daily numbers of new COVID-19 cases from January 17 to February 8, 2020 were used to determine the basic reproduction number (R0), peak date, doubling time, and daily growth rates in all five provinces. A sensitivity analysis was conducted to estimate epidemiological parameters. The highest and lowest number of deaths were observed in Hamedan (657 deaths) and Chaharmahal and Bakhtiari (54 deaths) provinces, respectively. The doubling time of confirmed cases in Kermanshah and Hamedan ranged widely from 18.59 days (95% confidence interval (CI): 17.38, 20) to 76.66 days (95% CI: 56.36, 119.78). In addition, the highest daily growth rates of confirmed cases were observed in Kermanshah (0.037, 95% CI: 0.034, 0.039) and Sistan and Baluchestan (0.032, 95% CI: 0.030, 0.034) provinces. In light of our findings, it is imperative to tailor containment strategies to the unique epidemiological profiles of each region in order to effectively mitigate the spread and impact of COVID-19. The wide variation in doubling times underscores the importance of flexibility in public health responses. By adapting measures to local conditions, we can better address the evolving dynamics of the pandemic and safeguard the well-being of communities.