Virological characteristics of SARS-CoV-2 Omicron BA.5.2.48 (preprint)

With the prevalence of sequentially-emerged sublineages including BA.1, BA.2 and BA.5, SARS-CoV-2 Omicron infection has transformed into a regional epidemic disease. As a sublineage of BA.5, the BA.5.2.48 outbreak and evolved into multi-subvariants in China without clearly established virological characteristics, especially the pathogenicity. Though reduced airborne transmission and pathogenicity of former Omicron sublineages have been revealed in animal models, the virological characteristics of BA.5.2.48 was unidentified. Here, we evaluated the in vitro and in vivo virological characteristics of two isolates of the prevalent BA.5.2.48 subvariant, DY.2 and DY.1.1 (a subvariant of DY.1). DY.2 replicates more efficiently than DY.1.1 in HelahACE2+ cells and Calu-3 cells. The A570S mutation (of DY.1) in a normal BA.5 spike protein (DY.2) leads to a 20% improvement in the hACE2 binding affinity, which is slightly reduced by a further K147E mutation (of DY.1.1). Compared to the normal BA.5 spike, the double-mutated protein demonstrates efficient cleavage and reduced fusogenicity. BA.5.2.48 demonstrated enhanced airborne transmission capacity in hamsters than BA.2. The pathogenicity of BA.5.2.48 is greater than BA.2, as revealed in K18-hACE2 rodents. Under immune selection pressure, DY.1.1 shows stronger fitness than DY.2 in hamster turbinates. Thus the outbreaking prevalent BA.5.2.48 multisubvariants exhibites divergent virological features.

Revisiting a COVID-19 seroprevalence cohort survey among health care workers and their household members in Kinshasa, DR Congo, 2020-2022 (preprint)

Serological surveys provide the most direct measurement to define the immunity landscape for many infectious diseases, including COVID-19, yet this methodology remains underexploited to clarify transmission dynamics. This is specifically the case in the context of the Democratic Republic of Congo, where COVID-19 case presentation was apparently largely oligo- or asymptomatic, and vaccination coverage remained extremely low. A cohort of 635 health care workers from 5 health zones of Kinshasa and 670 of their household members was followed up between July 2020 and January 2022, with 6- to 8-week intervals in the first year and 4- and 8-month intervals in the last year. At each visit, information on risk exposure and a blood sample were collected. Serology was defined as positive when binding antibodies against SARS-CoV-2 spike and nucleocapsid proteins were simultaneously present. The anti-SARS-CoV-2 antibody seroprevalence was high at baseline, at 17.3% (95% CI 14.4–20.6) and 7.8% (95% CI 5.5–10.8) for health care workers and household members, respectively, and fluctuated over time, between 9% and 62.1%. Seropositivity was heterogeneously distributed over the health zones (p < 0.001), ranging from 12.5% (95% CI 6.6–20.8) in N’djili to 33.7% (95% CI 24.6–43.8) in Bandalungwa at baseline for health care workers. Seropositivity was associated with increasing rounds aOR 1.75 (95% CI 1.66–1.85), with increasing age aOR 1.11 (95% CI 1.02–1.20), being a female aOR 1.35 (95% CI 1.10–1.66) and being a health care worker aOR 2.38 (95% CI 1.80–3.14). There was no evidence that health care workers brought the COVID-19 infection back home, with increased seropositivity risk among household members in subsequent surveys. There was much seroreversion and seroconversion detected over the different surveys, and health care workers had a 40% lower probability of seroreverting than household members (aOR 0.60 (95% CI 0.42–0.86)). Based on the WHO guidelines on the potential use of sero-surveys, the results of this cohort were revisited, and evidence provided by such studies in a ‘new disease’ epidemic and in a setting with low molecular testing capacities, such as COVID-19 in DRCongo, was insufficient to guide policy makers for defining control strategies.

Clinical and Epidemiological Features, Mechanism of Action, and Diagnosis of Global Pandemic Infectious Disease, COVID-19, and its Comparison with SARS, MERS, and H1N1: A Brief Overview (preprint)

The coronavirus disease 2019 (COVID-19) is a novel global pandemic infectious disease with higher potential for outbreaks than the other epidemic disease such as severe acute respiratory syndrome (SARS), influenza A (H1N1), and the Middle East respiratory syndrome (MERS), which identified in China on December 31, 2019. This disease is caused by a new generation of betacoronavirus termed as the 2019 novel coronavirus (2019-nCoV) or SARS-CoV-2. Although, the first report of this disease was in recent months, now, the COVID-19 is known as a global pandemics. Hence, the aim of this article is the quick review of the recent studies on the novel coronavirus disease 2019 including researches on the epidemiological parameters, mechanism of action, diagnosis, and treatment of the novel coronavirus disease, as well as clinical features of patients infected with COVID-19. Moreover, the novel COVID-19 has comprised of SARS, H1N1, and MERS.

Reporting delays: a widely neglected impact factor in COVID-19 forecasts (preprint)

Epidemic forecasts are only as good as the accuracy of epidemic measurements. Is epidemic data, particularly COVID-19 epidemic data, clean and devoid of noise? Common sense implies the negative answer. While we cannot evaluate the cleanliness of the COVID-19 epidemic data in a holistic fashion, we can assess the data for the presence of reporting delays. In our work, through the analysis of the first COVID-19 wave, we find substantial reporting delays in the published epidemic data. Motivated by the desire to enhance epidemic forecasts, we develop a statistical framework to detect, uncover, and remove reporting delays in the infectious, recovered, and deceased epidemic time series. Our framework can uncover and analyze reporting delays in 8 regions significantly affected by the first COVID-19 wave. Further, we demonstrate that removing reporting delays from epidemic data using our statistical framework may decrease the error in epidemic forecasts. While our statistical framework can be used in combination with any epidemic forecast method that intakes infectious, recovered, and deceased data, to make a basic assessment, we employed the classical SIRD epidemic model. Our results indicate that the removal of reporting delays from the epidemic data may decrease the forecast error by up to 50. We anticipate that our framework will be indispensable in the analysis of novel COVID-19 strains and other existing or novel infectious diseases.

Integrative Modeling and Analysis of the Interplay Between Epidemic and News Propagation Processes (preprint)

The COVID-19 pandemic has witnessed the role of online social networks (OSNs) in the spread of infectious diseases. The rise in severity of the epidemic augments the need for proper guidelines, but also promotes the propagation of fake news-items. The popularity of a news-item can reshape the public health behaviors and affect the epidemic processes. There is a clear inter-dependency between the epidemic process and the spreading of news-items. This work creates an integrative framework to understand the interplay. We first develop a population-dependent `saturated branching process' to continually track the propagation of trending news-items on OSNs. A two-time scale dynamical system is obtained by integrating the news-propagation model with SIRS epidemic model, to analyze the holistic system. It is observed that a pattern of periodic infections emerges under a linear behavioral influence, which explains the waves of infection and reinfection that we have experienced in the pandemic. We use numerical experiments to corroborate the results and use Twitter and COVID-19 data-sets to recreate the historical infection curve using the integrative model.

Bridging the gap_Estimation of 2022/2023 SARS-CoV-2 healthcare burden in Germany based on multidimensional data from a rapid epidemic panel (preprint)

Throughout the SARS-CoV-2 pandemic, Germany lacked an adaptive population panel for epidemic diseases and a modelling platform to rapidly incorporate panel estimates. We evaluated how a cross-sectional analysis of 9922 participants of the MuSPAD study in June/July 2022 combined with a newly developed modelling platform could bridge the gap and analyzed antibody levels, neutralizing serum activity and interferon-gamma release response of serum samples. We categorized the population into four groups with differing protection against severe course of disease (validated by neutralizing serum activity), and found that 30% were in the group with highest protection, and 85% in either the highest categories or second highest group regarding protection level. Estimated hospitalizations due to SARS-CoV-2 were predicted to be between 30 to 300% of the peak in 02/2021 dependent on assumed variant characteristics. We showed the feasibility of a rapid epidemic panel able to evaluate complex endpoints for SARS-CoV-2 and inform scenario modelling.

Multiparameter mobile blood analyzer for complete blood count utilizing contrast-enhanced defocusing imaging (preprint)

Blood analysis, though complete blood count (CBC), is the most basic medical test for disease diagnosis and health monitoring. However, the need for bulky and expensive laboratory facilities and skilled technicians limits the universal medical practices based on blood analysis outside of well-equipped laboratory environments. Here, we proposed a multiparameter mobile blood analyzer utilizing label-free contrast-enhanced defocusing imaging (CEDI) with the assistance of machine vision for instant and on-site blood analysis. The analyzer using CEDI obtains both the blood cell morphological characteristics and hemoglobin spectrophotometric information for simultaneous five-part white blood cell differential count, red blood cell count, and hemoglobin quantification without the need for sample staining. In addition, we have designed a miniature microscope with a sub-micron spatial resolution of (~0.98 μm), which is quite small, lightweight, and cost-effective for blood imaging. We have shown that our assay can analyze a sample within 10 minutes, using just a drop of fingertip blood (~7 μl) and measurements (30 samples) from the analyzer have strong correlations with clinical reference values (significant level: P < 0.0001). The proposed mobile blood analyzer can help provide universal access to blood analysis and has great potential for integrated surveillance of various epidemic diseases, including coronavirus infection, invermination, and anemia, especially in low-and middle-income countries.

Research on Resilience Assessment of Urban Epidemic Prevention Complex Network Driven by Improved SEIR Model (preprint)

Due to the unpredictability of Covid-19 and the complexity of community transmission, the existing theories and methods of system resilience assessment can not meet the needs of city epidemic prevention complex network resilience assessment. Therefore, this paper constructs a epidemic transmission complex network and studies the epidemic transmission model on the network---the Improved SEIR. And the paper predicts the development of the epidemic, point out the vulnerability of the network and the vulnerability of epidemic prevention. Then, according to the network structure of epidemic prevention and the results of SEIR model, the new theory and method of epidemic prevention resilience assessment in City A were constructed, and the resilience assessment of complex epidemic prevention network was divided into two dimensions: network structure resilience and urban function resilience. The new methods for the city epidemic prevention complex network resilience assessment are simulationed through measured data. The results show that when the epidemic comes, improving the resilience of urban epidemic prevention functions has a better effect on epidemic prevention and control. The paper integrates improved SEIR models with urban anti-epidemic measures, providing a new approach to resilience assessment of complex systems.

Extending SIRS epidemics to allow for gradual waning of immunity (preprint)

SIRS epidemic models assume that individual immunity (from infection and vaccination) wanes in one big leap, from complete immunity to complete susceptibility. For many diseases immunity on the contrary wanes gradually, something that's become even more evident during COVID-19 pandemic where also recently infected have a reinfection risk, and where booster vaccines are given to increase immunity. This paper considers an epidemic model allowing for such gradual waning of immunity (either linear or exponential waning) thereby extending SIRS epidemics, and also incorporates vaccination. The two versions for gradual waning of immunity are compared with the classic SIRS epidemic, where the three models are calibrated by having the same \emph{average cumulative immunity}. All models are shown to have identical basic reproduction number $R_0$. However, if no prevention is put in place, the exponential waning model has highest prevalence and the classic SIRS model has lowest. Similarly, the amount of vaccine supply needed to reach and maintain herd immunity is highest for the model with exponential decay of immunity and lowest for the classic SIRS model. consequently, if truth lies close to exponential (or linear) decay of immunity, expressions based on the SIRS epidemic will underestimate the endemic level and the critical vaccine supply will not be sufficient to reach and maintain herd immunity. For parameter choices fitting to COVID-19, the critical amount of vaccine supply is about 50% higher if immunity wanes linearly, and more than 150% higher when immunity wanes exponentially, as compared to the classic SIRS epidemic model.

Reconstructing the SARS-CoV-2 epidemic in eastern Uganda through longitudinal serosurveillance in a malaria cohort (preprint)

ABSTRACT Importance Estimating the true burden of SARS-CoV-2 infection has been difficult in sub-Saharan Africa due to asymptomatic infections and inadequate testing capacity. Antibody responses from serologic surveys can provide an estimate of SARS-CoV-2 exposure at the population level. Objective To estimate SARS-CoV-2 seroprevalence, attack rates, and re-infection in eastern Uganda using serologic surveillance from 2020 to early 2022. Design Plasma samples from participants in the Program for Resistance, Immunology, Surveillance, and Modeling of Malaria in Uganda (PRISM) Border Cohort were obtained at four sampling intervals: October-November 2020; March-April 2021; August-September 2021; and February-March 2022. Setting: Tororo and Busia districts, Uganda. Participants 1,483 samples from 441 participants living in 76 households were tested. Each participant contributed up to 4 time points for SARS-CoV-2 serology, with almost half of all participants contributing at all 4 time points, and almost 90% contributing at 3 or 4 time points. Information on SARS-CoV-2 vaccination status was collected from participants, with the earliest reported vaccinations in the cohort occurring in May 2021. Main Outcome(s) and Measure(s) The main outcomes of this study were antibody responses to the SARS-CoV-2 spike protein as measured with a bead-based serologic assay. Individual-level outcomes were aggregated to population-level SARS-CoV-2 seroprevalence, attack rates, and boosting rates. Estimates were weighted by the local age distribution based on census data. Results By the end of the Delta wave and before widespread vaccination, nearly 70% of the study population had experienced SARS-CoV-2 infection. During the subsequent Omicron wave, 85% of unvaccinated, previously seronegative individuals were infected for the first time, and ∼50% or more of unvaccinated, already seropositive individuals were likely re-infected, leading to an overall 96% seropositivity in this population. Our results suggest a lower probability of re-infection in individuals with higher pre-existing antibody levels. We found evidence of household clustering of SARS-CoV-2 seroconversion. We found no significant associations between SARS-CoV-2 seroconversion and gender, household size, or recent Plasmodium falciparum malaria exposure. Conclusions and Relevance Findings: from this study are consistent with very high infection rates and re-infection rates for SARS-CoV-2 in a rural population from eastern Uganda throughout the pandemic.

Periodic epidemic outbursts explained by local saturation of clusters (preprint)

Adding the notion of spatial locality to the susceptible-infected-removed (or SIR) model, allows to capture local saturation of an epidemic. The resulting minimum model of an epidemic, consisting of five ordinary differential equations with constant model coefficients, reproduces slowly decaying periodic outbursts, as observed in the COVID-19 or Spanish flu epidemic. It is shown that if immunity decays, even slowly, the model yields a fully periodic dynamics.

A Global Study of Screening Intensity and Economic Status on Epidemic Control Performance During Various Epidemic Periods of COVID-19 Mutant Strains (preprint)

This study analyzed global data and provides insights how economic conditions in various countries associate with epidemic control measures during different epidemic periods by mutant strains. In this study, the elasticity coefficient is estimated through a log-log model, which represents the percent change of the confirmed case number with respect to a percent change of the total number of screening tests in a country for epidemic control. The elasticity estimate was used to show the effectiveness of epidemic control by community screening. The 7-day rolling data of screening tests and confirmed cases from the Our World in Data (OWID) database for the pandemic periods of Alpha strain in 2020, Delta strain in 2021, and Omicron strain in 2022, suggests that the magnitude of the elasticity was associated with the economic condition of a country. Compared with the result s during either Alpha - or Delta- pandemic period, the Omicron pandemic has a much higher estimated elasticity coefficient of 1.317 (Alpha: 0.827 and Delta: 0.885). Further comparison of economic conditions that were classified by quartile ranges, the result reveals the elasticity in countries with GDP per capita between $11,354 and $26,651 or GDP per capita above $26,651 is statistically significantly lower than that in countries having GDP per capita below $3,335. The findings of this study imply that the performance of epidemic control in a country is not only dependent on epidemiological measures applied, but is also influenced by the economic condition of a country.

Inference of an Epidemic Driven by Random Transmission Rate: Explaining the Dynamics of COVID-19 in Bogotá (preprint)

The epidemic disease model is often used to predict an epidemic's characteristics and help plan an effective control strategy. Motivated by recent COVID-19 outbreaks, we develop and mathematically analyze a simple stochastic model that considers random perturbations in transmission rates and shows its validity using data from the Colombian city of Bogota. The stochastic epidemic model is stratified in the Susceptible-Exposed-Infectious-Recovered, \textit{SEIR}, type compartmental model with the randomness depicting the impact of stochastic variations due to external factors such as changes in environmental conditions and human behaviors that may impact the dynamics of an infectious disease.   The analysis resulted in derivation of approximate distribution of \textit{eventual extinction of infection state}, \textit{persistence of infection in the mean}, and \textit{the quasi-stationary infectious state}). Finally, we illustrate inferences and model parameters using reported COVID-19 epidemic data from the Colombian city of Bogotá. The outbreak in Bogota is divided into six distinct periods, with transmission rates high during the initiation of the epidemic and even much higher during the last period, potentially due to the rapid spread of the Omicron variant in Colombia still has a low vaccination rate. Mathematics Subject Classification: 92D30

The Effect of Analytical Thinking on Irrational Behaviors in Times of COVID-19: the Mediating Role of Misinformation Beliefs (preprint)

The present study was to investigate the influence of analytical thinking on irrational behaviors evoked by COVID-19 (including excessive epidemic prevention and hoarding behavior) and the mediating role of misinformation beliefs related to COVID-19 (including social and pseudoscientific beliefs) in a public health crisis. 1968 Chinese participants completed the Analytical Thinking Test, Misinformation Beliefs Questionnaire and Irrational Behavior Questionnaire online. The results showed that (1) Analytical thinking had a negative prediction on both excessive epidemic prevention and hoarding behavior; (2) misinformation beliefs, even social misinformation beliefs, partly mediated the relationship between the analytical thinking and irrational behaviors. These findings provide some useful references for effectively reducing people's irrational behaviors during a public health crisis.

The Effect of Analytical Thinking on Irrational Behaviors in Times of COVID-19: the Mediating Role of Misinformation Beliefs (preprint)

The present study was to investigate the influence of analytical thinking on irrational behaviors evoked by COVID-19 (including excessive epidemic prevention and hoarding behavior) and the mediating role of misinformation beliefs related to COVID-19 (including social and pseudoscientific beliefs) in a public health crisis. 1968 Chinese participants completed the Analytical Thinking Test, Misinformation Beliefs Questionnaire and Irrational Behavior Questionnaire online. The results showed that (1) Analytical thinking had a negative prediction on both excessive epidemic prevention and hoarding behavior; (2) misinformation beliefs, even social misinformation beliefs, partly mediated the relationship between the analytical thinking and irrational behaviors. These findings provide some useful references for effectively reducing people's irrational behaviors during a public health crisis.

Uncertainty and error in SARS-CoV-2 epidemiological parameters inferred from population-level epidemic models (preprint)

During the SARS-CoV2 pandemic, epidemic models have been central to policy-making. Public health responses have been shaped by model-based projections and inferences, especially related to the impact of various non-pharmaceutical interventions. Accompanying this has been increased scrutiny over model performance, model assumptions, and the way that uncertainty is incorporated and presented. Here we consider a population-level model, focusing on how distributions representing host infectiousness and the infection-to-death times are modelled, and particularly on the impact of inferred epidemic characteristics if these distributions are misspecified. We introduce an SIR -type model with the infected population structured by ‘infected age’, i.e. the number of days since first being infected, a formulation that enables distributions to be incorporated that are consistent with clinical data. We show that inference based on simpler models without infected age, which implicitly misspecify these distributions, leads to substantial errors in inferred quantities relevant to policy-making, such as the reproduction number and the impact of interventions. We consider uncertainty quantification via a Bayesian approach, implementing this for both synthetic and real data focusing on UK data in the period 15 Feb–14 Jul 2020, and emphasising circumstances where it is misleading to neglect uncertainty.

FAIR Sharing of Reproducible Models of Epidemic and Pandemic Forecast (preprint)

A major challenge for the dissemination, replication, and reuse of epidemiological forecasting studies during COVID-19 pandemics is the lack of clear guidelines and platforms to exchange models in a Findable, Accessible, Interoperable, and Reusable (FAIR) manner, facilitating reproducibility of research outcomes. During the beginning of pandemics, models were developed in diverse tools that were not interoperable, opaque without traceability and semantics, and scattered across various platforms - making them hard to locate, infer and reuse. In this work, we demonstrate that implementing the standards developed by the systems biology community to encode and share COVID-19 epidemiological models can serve as a roadmap to implement models as a tool in medical informatics, in general. As a proof-of-concept, we encoded and shared 24 epidemiological models using the standard format for model exchange in systems biology, annotated them with cross-references to data resources, packed up all associated files in COMBINE archives for easy sharing, and finally, disseminated the models through BioModels repository to significantly enhance their reproducibility and repurposing potential. We recommend the use of systems biology standards to encode and share models of epidemic and pandemic forecasts to improve their findability, accessibility, interoperability, reusability, and reproducibility.

Prophylactic and reactive vaccination strategies for healthcare workers against MERS-CoV (preprint)

Several vaccines candidates are in development against Middle East respiratory syndrome–related coronavirus (MERS-CoV), which remains a major public health concern. Using individual-level data on the 2013-2014 Kingdom of Saudi Arabia epidemic, we employ counterfactual analysis on inferred transmission trees (“who-infected-whom”) to assess potential vaccine impact. We investigate the conditions under which prophylactic “proactive” campaigns would outperform “reactive” campaigns (i.e. vaccinating either before or in response to the next outbreak), focussing on healthcare workers. Spatial scale is crucial: if vaccinating healthcare workers in response to outbreaks at their hospital only, proactive campaigns perform better, unless efficacy has waned significantly. However, campaigns that react at regional or national level consistently outperform proactive campaigns. Measures targeting the animal reservoir reduce transmission linearly, albeit with wide uncertainty. Substantial reduction of MERS-CoV morbidity and mortality is possible when vaccinating healthcare workers, underlining the need for at-risk countries to stockpile vaccines when available.

Epidemic Trend Analysis of SARS-CoV-2 in SAARC Countries Using Modified SIR (M-SIR) Predictive Model (preprint)

A novel coronavirus causing the severe and fatal respiratory syndrome was identified in China, is now producing outbreaks in more than two hundred countries around the world, and became pandemic by the time. In this article, a modified version of the well-known mathematical epidemic model Susceptible (S)- Infected (I)- Recovered (R) is used to analyze the epidemic’s course of COVID-19 in eight different countries of the South Asian Association for Regional Cooperation (SAARC). To achieve this goal, the parameters of the SIR model are identified by using publicly available data for the corresponding countries: Afghanistan, Bangladesh, Bhutan, India, the Maldives, Nepal, Pakistan, and Sri Lanka. Based on the prediction model we estimated the epidemic trend of COVID-19 outbreak in SAARC countries for 20 days, 90 days, and 180 days respectively. An SML (short-mid-long) term prediction model has been designed to understand the early dynamics of the COVID-19 Epidemic in the southeast Asian region. The maximum and minimum basic reproduction numbers (R0 = 1.33 and 1.07) for SAARC countries are predicted to be in Pakistan and Bhutan. We equate simulation results with real data in the SAARC countries on the COVID-19 outbreak, and model potential countermeasure implementation scenarios. Our results should provide policymakers with a method for evaluating the impacts of possible interventions, including lockdown and social distancing, as well as testing and contact tracking.

An Innovative Electronic Sterilization System (S-Vehicle, NaOCI.5H2O and CeO2NP) for Epidemic Areas by Virus Covid-19 Manage Remotely using Mobil Application (preprint)

The aim of this work is to provide an integrated electronic developed system that is managed remotely by smartphones and is able to sterilize large areas affected by a specific virus. It can be transmitted between people by approaching or touching infected people, such as the Covid virus 19. The work includes treating two of the main challenges in this field: The first challenge is to find modern sterilization materials, and here we will resort to using oily materials for sterilization that are highly effective and light in weight that are easy to break into spray particles flying in places to be sterilized. As for the second challenge, sterilization operations using traditional methods often lead to the exposure of the personnel responsible for sterilization to infection with the virus, so the second goal is to develop marching vehicles controlled by mobile devices called (S-Vehicles) that can be operated during periods Specific, highly efficient and low cost, in addition to that it reduces the effort and time that can be spent by the rescue teams and reduces the cases of injuries as much as possible, especially since the work is completely managed automatically from the affected areas. The designed S-Vehicles consists of several basic stages: The stage of building an integrated electronic circuit and controlling it remotely connected to it by a tank to carry the oily materials attached to the Microniar, which is responsible for breaking down the sterile oily substance into small, volatile spray atoms. In addition, the GPS will be linked with S-Vehicles in order to determine the exact location and coordinates of the area in which (S-Vehicles) begin to open the tank valve and begin the sterilization process. The most important points of the designed system: It reduces the time wasted in the process of sterilizing large areas, whether or not they are infected. The need for human effort diminishes, as it used to be that a group of people are deployed from disaster areas to sterilize these places, which increases the risk of infection for these people. The designed S-Vehicles are easy to move in any direction, as well as they can be used to sterilize hospitals, streets, and roads and have the advantage of being of low cost compared to other means. The compound material for the purpose of sterilization is characterized as an oily substance with light weights, which makes it easy to break it by micronise into very small spray atoms that are spread by S-Vehicles designed in different directions in the target area to be sterilized. The system can be managed completely automatically and controlled remotely. This integrated system is considered part of the electronic sterilization system intended to be applied in Iraq. The disinfection process aims to prevent new infections, eliminate viruses on surfaces and tools, and reduce their contamination with viruses. The design is strong and shock-resistant, and it can be used for other purposes, such as controlling crops by changing the type of solution used by placing a pesticide in the tank instead of the oil sterilization solution, but in both cases the solution must be characterized by light weight and durability of micronized in splitting the solution into fine volatile spray.